diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml
new file mode 100644
index 0000000000..f3586044ab
--- /dev/null
+++ b/.github/workflows/python-package-conda.yml
@@ -0,0 +1,34 @@
+name: Python Package using Conda
+
+on: [push]
+
+jobs:
+  build-linux:
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 5
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Add conda to system path
+      run: |
+        # $CONDA is an environment variable pointing to the root of the miniconda directory
+        echo $CONDA/bin >> $GITHUB_PATH
+    - name: Install dependencies
+      run: |
+        conda env update --file environment.yml --name base
+    - name: Lint with flake8
+      run: |
+        conda install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        conda install pytest
+        pytest
diff --git a/README.md b/README.md
index 6c090c8179..7629603e3a 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,6 @@
+https://github.com/mha2112/Deploying-a-Scalable-ML-Pipeline-with-FastAPI
+
+
 Working in a command line environment is recommended for ease of use with git and dvc. If on Windows, WSL1 or 2 is recommended.
 
 # Environment Set up (pip or conda)
@@ -9,10 +12,11 @@ Working in a command line environment is recommended for ease of use with git an
     * As you work on the code, continually commit changes. Trained models you want to use in production must be committed to GitHub.
 * Connect your local git repo to GitHub.
 * Setup GitHub Actions on your repo. You can use one of the pre-made GitHub Actions if at a minimum it runs pytest and flake8 on push and requires both to pass without error.
-    * Make sure you set up the GitHub Action to have the same version of Python as you used in development.
+    * Make sure you set up the GitHub Action to have the same version of Python as you used in development."
 
-# Data
-* Download census.csv and commit it to dvc.
+    
+# Data-----from chat===Ray, "The goal of that cleaning step was to remove those errors, but this iteration appears to have removed those... so there's really no point in cleaning the data at all. It's already in an optimal state."
+(((((((* Download census.csv and commit it to dvc.---------pg.2 in comments says do not have to do)))))))))
 * This data is messy, try to open it in pandas and see what you get.
 * To clean it, use your favorite text editor to remove all spaces.
 
diff --git a/local_api.py b/local_api.py
index a3bff2f988..189502184e 100644
--- a/local_api.py
+++ b/local_api.py
@@ -3,12 +3,16 @@
 import requests
 
 # TODO: send a GET using the URL http://127.0.0.1:8000
-r = None # Your code here
+
+
+#r = None # Your code here
+url = "http://127.0.0.1:8000"
+r = requests.get(url)
 
 # TODO: print the status code
-# print()
+print(r.status_code)
 # TODO: print the welcome message
-# print()
+print(r.json())
 
 
 
@@ -30,9 +34,10 @@
 }
 
 # TODO: send a POST using the data above
-r = None # Your code here
+#r = None # Your code here
+r = requests.post(f"{url}/predict",json=data)
 
 # TODO: print the status code
-# print()
+print(r.status_code)
 # TODO: print the result
-# print()
+print(r.json())
diff --git a/main.py b/main.py
index 638e2414de..6dd82244c6 100644
--- a/main.py
+++ b/main.py
@@ -4,8 +4,10 @@
 from fastapi import FastAPI
 from pydantic import BaseModel, Field
 
+from ml.model import save_model
 from ml.data import apply_label, process_data
 from ml.model import inference, load_model
+from ml.model import train_and_save_final_model
 
 # DO NOT MODIFY
 class Data(BaseModel):
@@ -26,25 +28,30 @@ class Data(BaseModel):
     hours_per_week: int = Field(..., example=40, alias="hours-per-week")
     native_country: str = Field(..., example="United-States", alias="native-country")
 
-path = None # TODO: enter the path for the saved encoder 
-encoder = load_model(path)
+#path = None # TODO: enter the path for the saved encoder
+encoder_path = "model/encoder.pkl"
+encoder = load_model(encoder_path)
 
-path = None # TODO: enter the path for the saved model 
-model = load_model(path)
+#path = None # TODO: enter the path for the saved model
+model_path = "model/model.pkl"
+model = load_model(model_path)
 
 # TODO: create a RESTful API using FastAPI
-app = None # your code here
+#app = None # your code here
+app = FastAPI()
+
 
 # TODO: create a GET on the root giving a welcome message
 @app.get("/")
 async def get_root():
     """ Say hello!"""
     # your code here
+    return{"message":"Hello!"}
     pass
 
 
 # TODO: create a POST on a different path that does model inference
-@app.post("/data/")
+@app.post("/predict/")
 async def post_inference(data: Data):
     # DO NOT MODIFY: turn the Pydantic model into a dict.
     data_dict = data.dict()
@@ -66,9 +73,18 @@ async def post_inference(data: Data):
     ]
     data_processed, _, _, _ = process_data(
         # your code here
+        X=data,
+        categorical_features=cat_features,
+        label=None,
+        training=False,
+        encoder=encoder,
         # use data as data input
         # use training = False
         # do not need to pass lb as input
     )
-    _inference = None # your code here to predict the result using data_processed
+    #_inference = None # your code here to predict the result using data_processed
+    _inference = inference(model, data_processed)
     return {"result": apply_label(_inference)}
+
+
+
diff --git a/ml/model.py b/ml/model.py
index f361110f18..fce9ce0479 100644
--- a/ml/model.py
+++ b/ml/model.py
@@ -2,6 +2,11 @@
 from sklearn.metrics import fbeta_score, precision_score, recall_score
 from ml.data import process_data
 # TODO: add necessary import
+import os
+from sklearn.ensemble import RandomForestClassifier
+import numpy as np
+import pandas as pd
+
 
 # Optional: implement hyperparameter tuning.
 def train_model(X_train, y_train):
@@ -20,6 +25,9 @@ def train_model(X_train, y_train):
         Trained machine learning model.
     """
     # TODO: implement the function
+    model = RandomForestClassifier(random_state=42)
+    model.fit(X_train, y_train)
+    return model 
     pass
 
 
@@ -60,6 +68,7 @@ def inference(model, X):
         Predictions from the model.
     """
     # TODO: implement the function
+    return model.predict(X)
     pass
 
 def save_model(model, path):
@@ -73,11 +82,16 @@ def save_model(model, path):
         Path to save pickle file.
     """
     # TODO: implement the function
+    with open(path,"wb") as f:
+        pickle.dump(model,f)
     pass
 
 def load_model(path):
     """ Loads pickle file from `path` and returns it."""
     # TODO: implement the function
+    print(f'loading model path {path}')
+    with open(path,"rb") as f:
+        return pickle.load(f)
     pass
 
 
@@ -117,12 +131,50 @@ def performance_on_categorical_slice(
     fbeta : float
 
     """
+    sliced_data = data[data[column_name] == slice_value]
+    
     # TODO: implement the function
     X_slice, y_slice, _, _ = process_data(
         # your code here
         # for input data, use data in column given as "column_name", with the slice_value 
         # use training = False
+        sliced_data,
+        categorical_features=categorical_features,
+        label=label,
+        training=False,
+        encoder=encoder,
+        lb=lb,
     )
-    preds = None # your code here to get prediction on X_slice using the inference function
+    #preds = None # your code here to get prediction on X_slice using the inference function
+    preds = inference(model, X_slice)
     precision, recall, fbeta = compute_model_metrics(y_slice, preds)
     return precision, recall, fbeta
+
+#added for k-fold model
+def train_and_save_final_model(data, categorical_features, label, model_dir):
+    X_all, y_all, encoder, lb = process_data(
+        data,
+        categorical_features=categorical_features,
+        label=label,
+        training=True,
+    )
+    
+    model = train_model(X_all, y_all)
+    
+    os.makedirs(model_dir, exist_ok=True)
+    model_path = os.path.join(model_dir, "model.pk1")
+    encoder_path= os.path.join(model_dir, "encoder.pkl")
+    lb_path= os.path.join(model_dir, "lb.pkl")
+    
+    save_model(model, model_path)
+    save_model(encoder, encoder_path)
+    save_model(lb,lb_path)
+    
+    #didn't see model_saved after running the code
+    print(f'Model saved to {model_path}')
+    print(f'Model saved to {encoder_path}')
+    print(f'Model saved to {lb_path}')
+    
+
+    return model, encoder, lb
+    
\ No newline at end of file
diff --git a/model/encoder.pkl b/model/encoder.pkl
new file mode 100644
index 0000000000..0fe0330a06
Binary files /dev/null and b/model/encoder.pkl differ
diff --git a/model/lb.pkl b/model/lb.pkl
new file mode 100644
index 0000000000..c7f9fa531b
Binary files /dev/null and b/model/lb.pkl differ
diff --git a/model/model.pkl b/model/model.pkl
new file mode 100644
index 0000000000..d9c6766cfd
Binary files /dev/null and b/model/model.pkl differ
diff --git a/model_card_template.md b/model_card_template.md
index 0392f3b9eb..bf41b6d3fc 100644
--- a/model_card_template.md
+++ b/model_card_template.md
@@ -3,16 +3,52 @@
 For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
 
 ## Model Details
+Salary prediction from census data using a random forest classifier. Random forest uses multiple decision trees to train the data that improves training efficiency being an ensemble. 
+OneHotEncoding is used for the categorical column information and the labelBinarizer for the Salary column.
 
 ## Intended Use
+Salary prediction from census data shows what common categories make less than or greater than 50K a year. 
 
 ## Training Data
-
+The model used k-fold validation with standard k=5 parameter. Consistency metrics are done on the slicing of the k-folds.
 ## Evaluation Data
+When the model predicts the salary to be over 50K with applied variables at testing it is 73% of time, correct.
+From the slice_output.txt file there are some categories that have a higher count creating metrics that come together, but the under represented categories with few counts have a high metric in one score and a low in the others:
+
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+
+the own=child category has a high count and a low recall and f1
+everyone in this category did make over 50K, when predicting over 50K it is 100% right. But it still underpredicts with 1,019 samples. 
+
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+
+Native country being Cambodia only has 3 people representing that country so Precision is high but recall and f1 are low. The sample is small. The test data found 1 that made over 50K from Cambodia, but recall and f1 are zero showing not definite or confident results for this categoric data. 
+
 
 ## Metrics
 _Please include the metrics used and your model's performance on those metrics._
+Precision, Recall and F1 score were used to test the model with the following values:
+
+Precision: 0.7451 | Recall: 0.6308 | F1: 0.6832
+Precision: 0.7087 | Recall: 0.6164 | F1: 0.6593
+Precision: 0.7346 | Recall: 0.6180 | F1: 0.6713
+Precision: 0.7475 | Recall: 0.6222 | F1: 0.6791
+Precision: 0.7453 | Recall: 0.6307 | F1: 0.6832
+
+The average of these scores were calculated as:
+avg precision:  0.7363
+avg recall:  0.6236
+avg f1_score:  0.6752
+
+Precision being what values were actually correcty or true positive is 73%
+recall or sensitivity of values that could be missed is 62%
+F1 score the balance between both precision and recall is 67%
 
 ## Ethical Considerations
+The model will predict salary based on the other categorical information and is using performance slicing to id bias or gender, sex, race. 
 
 ## Caveats and Recommendations
+The missing values could even be question marks: occupation: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294, this slice_output found 39 question marks for the census question occupattion. The data is not clean and can create distraction from the true numbers or data analyzed. 
\ No newline at end of file
diff --git a/python3 b/python3
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/screenshots/continuous_integration.png b/screenshots/continuous_integration.png
new file mode 100644
index 0000000000..5b64b3180a
Binary files /dev/null and b/screenshots/continuous_integration.png differ
diff --git a/screenshots/local_api.png b/screenshots/local_api.png
new file mode 100644
index 0000000000..a8ee44733c
Binary files /dev/null and b/screenshots/local_api.png differ
diff --git a/screenshots/unit_test.png b/screenshots/unit_test.png
new file mode 100644
index 0000000000..fe21bfd653
Binary files /dev/null and b/screenshots/unit_test.png differ
diff --git a/slice_output.txt b/slice_output.txt
new file mode 100644
index 0000000000..a0a2494b2f
--- /dev/null
+++ b/slice_output.txt
@@ -0,0 +1,5020 @@
+workclass: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+workclass: Federal-gov, Count: 191
+Precision: 0.7571 | Recall: 0.7571 | F1: 0.7571
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6545 | F1: 0.6990
+workclass: Private, Count: 4,578
+Precision: 0.7447 | Recall: 0.6324 | F1: 0.6840
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7565 | Recall: 0.7373 | F1: 0.7468
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7339 | Recall: 0.5096 | F1: 0.6015
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6575 | F1: 0.7007
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.4286 | Recall: 0.2500 | F1: 0.3158
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.6923 | Recall: 0.5745 | F1: 0.6279
+education: Assoc-voc, Count: 273
+Precision: 0.6667 | Recall: 0.5397 | F1: 0.5965
+education: Bachelors, Count: 1,053
+Precision: 0.7564 | Recall: 0.7244 | F1: 0.7401
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6622 | Recall: 0.4261 | F1: 0.5185
+education: Masters, Count: 369
+Precision: 0.8309 | Recall: 0.8309 | F1: 0.8309
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8191 | Recall: 0.9167 | F1: 0.8652
+education: Some-college, Count: 1,485
+Precision: 0.6952 | Recall: 0.5271 | F1: 0.5996
+marital-status: Divorced, Count: 920
+Precision: 0.7826 | Recall: 0.3495 | F1: 0.4832
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7377 | Recall: 0.6816 | F1: 0.7085
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8182 | Recall: 0.4369 | F1: 0.5696
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+occupation: Adm-clerical, Count: 726
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6641 | Recall: 0.4696 | F1: 0.5502
+occupation: Exec-managerial, Count: 838
+Precision: 0.8011 | Recall: 0.7506 | F1: 0.7750
+occupation: Farming-fishing, Count: 193
+Precision: 0.6364 | Recall: 0.2500 | F1: 0.3590
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5938 | Recall: 0.4043 | F1: 0.4810
+occupation: Other-service, Count: 667
+Precision: 0.8571 | Recall: 0.2308 | F1: 0.3636
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7882 | Recall: 0.7500 | F1: 0.7686
+occupation: Protective-serv, Count: 136
+Precision: 0.6970 | Recall: 0.5476 | F1: 0.6133
+occupation: Sales, Count: 729
+Precision: 0.7333 | Recall: 0.6875 | F1: 0.7097
+occupation: Tech-support, Count: 189
+Precision: 0.6744 | Recall: 0.5686 | F1: 0.6170
+occupation: Transport-moving, Count: 317
+Precision: 0.6000 | Recall: 0.4219 | F1: 0.4954
+relationship: Husband, Count: 2,590
+Precision: 0.7401 | Recall: 0.6838 | F1: 0.7108
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8021 | Recall: 0.4096 | F1: 0.5423
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9231 | Recall: 0.2667 | F1: 0.4138
+relationship: Wife, Count: 322
+Precision: 0.7164 | Recall: 0.6713 | F1: 0.6931
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6250 | Recall: 0.5000 | F1: 0.5556
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7547 | Recall: 0.6452 | F1: 0.6957
+race: Black, Count: 599
+Precision: 0.7692 | Recall: 0.6154 | F1: 0.6838
+race: Other, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: White, Count: 5,595
+Precision: 0.7448 | Recall: 0.6317 | F1: 0.6836
+sex: Female, Count: 2,126
+Precision: 0.7267 | Recall: 0.5021 | F1: 0.5939
+sex: Male, Count: 4,387
+Precision: 0.7476 | Recall: 0.6532 | F1: 0.6972
+native-country: ?, Count: 125
+Precision: 0.7097 | Recall: 0.7097 | F1: 0.7097
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 22
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 0.8750 | F1: 0.9333
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8333 | Recall: 0.7692 | F1: 0.8000
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.7778 | Recall: 0.8750 | F1: 0.8235
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6250 | F1: 0.7692
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7448 | Recall: 0.6265 | F1: 0.6805
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 368
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+workclass: Federal-gov, Count: 186
+Precision: 0.7424 | Recall: 0.6447 | F1: 0.6901
+workclass: Local-gov, Count: 475
+Precision: 0.6912 | Recall: 0.6714 | F1: 0.6812
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,513
+Precision: 0.6974 | Recall: 0.6033 | F1: 0.6469
+workclass: Self-emp-inc, Count: 231
+Precision: 0.8319 | Recall: 0.7557 | F1: 0.7920
+workclass: Self-emp-not-inc, Count: 480
+Precision: 0.6598 | Recall: 0.5039 | F1: 0.5714
+workclass: State-gov, Count: 256
+Precision: 0.7188 | Recall: 0.7302 | F1: 0.7244
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 197
+Precision: 0.5000 | Recall: 0.0833 | F1: 0.1429
+education: 11th, Count: 235
+Precision: 0.8000 | Recall: 0.2857 | F1: 0.4211
+education: 12th, Count: 77
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 1st-4th, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 70
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 118
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 9th, Count: 84
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 225
+Precision: 0.6078 | Recall: 0.5741 | F1: 0.5905
+education: Assoc-voc, Count: 251
+Precision: 0.6341 | Recall: 0.4194 | F1: 0.5049
+education: Bachelors, Count: 1,071
+Precision: 0.7460 | Recall: 0.7600 | F1: 0.7529
+education: Doctorate, Count: 83
+Precision: 0.7812 | Recall: 0.8333 | F1: 0.8065
+education: HS-grad, Count: 2,157
+Precision: 0.5520 | Recall: 0.4220 | F1: 0.4783
+education: Masters, Count: 351
+Precision: 0.7949 | Recall: 0.7908 | F1: 0.7928
+education: Preschool, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: Prof-school, Count: 117
+Precision: 0.8696 | Recall: 0.9091 | F1: 0.8889
+education: Some-college, Count: 1,434
+Precision: 0.6761 | Recall: 0.4898 | F1: 0.5680
+marital-status: Divorced, Count: 853
+Precision: 0.8108 | Recall: 0.3448 | F1: 0.4839
+marital-status: Married-AF-spouse, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+marital-status: Married-civ-spouse, Count: 3,044
+Precision: 0.6967 | Recall: 0.6535 | F1: 0.6744
+marital-status: Married-spouse-absent, Count: 74
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+marital-status: Never-married, Count: 2,124
+Precision: 0.9070 | Recall: 0.3861 | F1: 0.5417
+marital-status: Separated, Count: 224
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+marital-status: Widowed, Count: 191
+Precision: 0.8889 | Recall: 0.4706 | F1: 0.6154
+occupation: ?, Count: 369
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+occupation: Adm-clerical, Count: 768
+Precision: 0.6364 | Recall: 0.4949 | F1: 0.5568
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 793
+Precision: 0.6000 | Recall: 0.4693 | F1: 0.5266
+occupation: Exec-managerial, Count: 806
+Precision: 0.7886 | Recall: 0.7500 | F1: 0.7688
+occupation: Farming-fishing, Count: 196
+Precision: 0.6000 | Recall: 0.3750 | F1: 0.4615
+occupation: Handlers-cleaners, Count: 271
+Precision: 0.6667 | Recall: 0.1053 | F1: 0.1818
+occupation: Machine-op-inspct, Count: 392
+Precision: 0.5789 | Recall: 0.2245 | F1: 0.3235
+occupation: Other-service, Count: 659
+Precision: 0.6667 | Recall: 0.1538 | F1: 0.2500
+occupation: Priv-house-serv, Count: 36
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 855
+Precision: 0.7454 | Recall: 0.7358 | F1: 0.7405
+occupation: Protective-serv, Count: 148
+Precision: 0.6170 | Recall: 0.6444 | F1: 0.6304
+occupation: Sales, Count: 686
+Precision: 0.6570 | Recall: 0.6175 | F1: 0.6366
+occupation: Tech-support, Count: 178
+Precision: 0.7037 | Recall: 0.7917 | F1: 0.7451
+occupation: Transport-moving, Count: 353
+Precision: 0.6122 | Recall: 0.3947 | F1: 0.4800
+relationship: Husband, Count: 2,652
+Precision: 0.7039 | Recall: 0.6520 | F1: 0.6770
+relationship: Not-in-family, Count: 1,639
+Precision: 0.8659 | Recall: 0.4176 | F1: 0.5635
+relationship: Other-relative, Count: 199
+Precision: 0.5000 | Recall: 0.1667 | F1: 0.2500
+relationship: Own-child, Count: 1,000
+Precision: 0.7500 | Recall: 0.2143 | F1: 0.3333
+relationship: Unmarried, Count: 673
+Precision: 0.7857 | Recall: 0.3143 | F1: 0.4490
+relationship: Wife, Count: 349
+Precision: 0.6564 | Recall: 0.6859 | F1: 0.6708
+race: Amer-Indian-Eskimo, Count: 67
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 205
+Precision: 0.7561 | Recall: 0.5536 | F1: 0.6392
+race: Black, Count: 600
+Precision: 0.6078 | Recall: 0.4366 | F1: 0.5082
+race: Other, Count: 52
+Precision: 0.6667 | Recall: 0.4000 | F1: 0.5000
+race: White, Count: 5,588
+Precision: 0.7102 | Recall: 0.6295 | F1: 0.6674
+sex: Female, Count: 2,195
+Precision: 0.6869 | Recall: 0.5738 | F1: 0.6253
+sex: Male, Count: 4,317
+Precision: 0.7124 | Recall: 0.6241 | F1: 0.6653
+native-country: ?, Count: 106
+Precision: 0.8182 | Recall: 0.6000 | F1: 0.6923
+native-country: Cambodia, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 26
+Precision: 0.7500 | Recall: 0.3750 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+native-country: Columbia, Count: 12
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 27
+Precision: 0.8750 | Recall: 0.7778 | F1: 0.8235
+native-country: Dominican-Republic, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: El-Salvador, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 20
+Precision: 0.7500 | Recall: 0.4286 | F1: 0.5455
+native-country: France, Count: 6
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Germany, Count: 20
+Precision: 0.6667 | Recall: 0.2857 | F1: 0.4000
+native-country: Greece, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Guatemala, Count: 10
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 12
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 2
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: India, Count: 24
+Precision: 0.8571 | Recall: 0.6667 | F1: 0.7500
+native-country: Iran, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Ireland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 11
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Jamaica, Count: 21
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 9
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 120
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Nicaragua, Count: 6
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 41
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+native-country: Poland, Count: 13
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Puerto-Rico, Count: 19
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Scotland, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: South, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: United-States, Count: 5,856
+Precision: 0.7068 | Recall: 0.6225 | F1: 0.6619
+native-country: Vietnam, Count: 13
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+workclass: ?, Count: 357
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+workclass: Federal-gov, Count: 195
+Precision: 0.7179 | Recall: 0.7887 | F1: 0.7517
+workclass: Local-gov, Count: 395
+Precision: 0.6939 | Recall: 0.5812 | F1: 0.6326
+workclass: Never-worked, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,566
+Precision: 0.7378 | Recall: 0.6123 | F1: 0.6692
+workclass: Self-emp-inc, Count: 225
+Precision: 0.7826 | Recall: 0.7500 | F1: 0.7660
+workclass: Self-emp-not-inc, Count: 514
+Precision: 0.7475 | Recall: 0.5000 | F1: 0.5992
+workclass: State-gov, Count: 255
+Precision: 0.7273 | Recall: 0.7089 | F1: 0.7179
+workclass: Without-pay, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 180
+Precision: 0.7500 | Recall: 0.5000 | F1: 0.6000
+education: 11th, Count: 242
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: 12th, Count: 85
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 1st-4th, Count: 42
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 72
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 160
+Precision: 0.5000 | Recall: 0.1000 | F1: 0.1667
+education: 9th, Count: 110
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 199
+Precision: 0.6889 | Recall: 0.5962 | F1: 0.6392
+education: Assoc-voc, Count: 283
+Precision: 0.8333 | Recall: 0.5233 | F1: 0.6429
+education: Bachelors, Count: 1,073
+Precision: 0.7332 | Recall: 0.7528 | F1: 0.7429
+education: Doctorate, Count: 85
+Precision: 0.7536 | Recall: 0.8966 | F1: 0.8189
+education: HS-grad, Count: 2,063
+Precision: 0.5864 | Recall: 0.3601 | F1: 0.4462
+education: Masters, Count: 329
+Precision: 0.8218 | Recall: 0.8034 | F1: 0.8125
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 120
+Precision: 0.9184 | Recall: 0.9677 | F1: 0.9424
+education: Some-college, Count: 1,458
+Precision: 0.6887 | Recall: 0.5069 | F1: 0.5840
+marital-status: Divorced, Count: 899
+Precision: 0.6512 | Recall: 0.3218 | F1: 0.4308
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 3,011
+Precision: 0.7336 | Recall: 0.6603 | F1: 0.6950
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+marital-status: Never-married, Count: 2,100
+Precision: 0.7800 | Recall: 0.4149 | F1: 0.5417
+marital-status: Separated, Count: 215
+Precision: 1.0000 | Recall: 0.4167 | F1: 0.5882
+marital-status: Widowed, Count: 207
+Precision: 1.0000 | Recall: 0.2222 | F1: 0.3636
+occupation: ?, Count: 359
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+occupation: Adm-clerical, Count: 755
+Precision: 0.6076 | Recall: 0.4615 | F1: 0.5246
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 849
+Precision: 0.6718 | Recall: 0.4378 | F1: 0.5301
+occupation: Exec-managerial, Count: 808
+Precision: 0.7836 | Recall: 0.7467 | F1: 0.7647
+occupation: Farming-fishing, Count: 201
+Precision: 0.7143 | Recall: 0.2273 | F1: 0.3448
+occupation: Handlers-cleaners, Count: 297
+Precision: 0.6667 | Recall: 0.2353 | F1: 0.3478
+occupation: Machine-op-inspct, Count: 408
+Precision: 0.6316 | Recall: 0.2400 | F1: 0.3478
+occupation: Other-service, Count: 626
+Precision: 0.7500 | Recall: 0.1200 | F1: 0.2069
+occupation: Priv-house-serv, Count: 32
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8000 | Recall: 0.7890 | F1: 0.7945
+occupation: Protective-serv, Count: 129
+Precision: 0.5714 | Recall: 0.5405 | F1: 0.5556
+occupation: Sales, Count: 729
+Precision: 0.6989 | Recall: 0.6468 | F1: 0.6718
+occupation: Tech-support, Count: 190
+Precision: 0.6721 | Recall: 0.6721 | F1: 0.6721
+occupation: Transport-moving, Count: 311
+Precision: 0.7059 | Recall: 0.4211 | F1: 0.5275
+relationship: Husband, Count: 2,694
+Precision: 0.7378 | Recall: 0.6656 | F1: 0.6998
+relationship: Not-in-family, Count: 1,648
+Precision: 0.7342 | Recall: 0.3742 | F1: 0.4957
+relationship: Other-relative, Count: 208
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+relationship: Own-child, Count: 969
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Unmarried, Count: 708
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Wife, Count: 285
+Precision: 0.7031 | Recall: 0.6294 | F1: 0.6642
+race: Amer-Indian-Eskimo, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 210
+Precision: 0.6383 | Recall: 0.5769 | F1: 0.6061
+race: Black, Count: 634
+Precision: 0.7581 | Recall: 0.5875 | F1: 0.6620
+race: Other, Count: 50
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+race: White, Count: 5,563
+Precision: 0.7378 | Recall: 0.6217 | F1: 0.6748
+sex: Female, Count: 2,124
+Precision: 0.7099 | Recall: 0.5251 | F1: 0.6037
+sex: Male, Count: 4,388
+Precision: 0.7381 | Recall: 0.6331 | F1: 0.6816
+native-country: ?, Count: 104
+Precision: 0.5714 | Recall: 0.6000 | F1: 0.5854
+native-country: Cambodia, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 25
+Precision: 0.7143 | Recall: 0.5556 | F1: 0.6250
+native-country: China, Count: 10
+Precision: 0.3333 | Recall: 1.0000 | F1: 0.5000
+native-country: Columbia, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 21
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Dominican-Republic, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 25
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: England, Count: 18
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: France, Count: 7
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Germany, Count: 27
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+native-country: Greece, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Holand-Netherlands, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 15
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: Iran, Count: 9
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Ireland, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 20
+Precision: 1.0000 | Recall: 0.5714 | F1: 0.7273
+native-country: Jamaica, Count: 14
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 16
+Precision: 0.5000 | Recall: 0.6667 | F1: 0.5714
+native-country: Laos, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Mexico, Count: 152
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+native-country: Nicaragua, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Outlying-US(Guam-USVI-etc), Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 37
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Poland, Count: 8
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Portugal, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 12
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Taiwan, Count: 14
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,818
+Precision: 0.7428 | Recall: 0.6205 | F1: 0.6762
+native-country: Vietnam, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 345
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+workclass: Federal-gov, Count: 200
+Precision: 0.7973 | Recall: 0.7375 | F1: 0.7662
+workclass: Local-gov, Count: 421
+Precision: 0.7434 | Recall: 0.6667 | F1: 0.7029
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,547
+Precision: 0.7450 | Recall: 0.5990 | F1: 0.6641
+workclass: Self-emp-inc, Count: 214
+Precision: 0.7769 | Recall: 0.8211 | F1: 0.7984
+workclass: Self-emp-not-inc, Count: 511
+Precision: 0.6667 | Recall: 0.5035 | F1: 0.5737
+workclass: State-gov, Count: 270
+Precision: 0.8364 | Recall: 0.6866 | F1: 0.7541
+workclass: Without-pay, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 10th, Count: 184
+Precision: 0.5000 | Recall: 0.1538 | F1: 0.2353
+education: 11th, Count: 240
+Precision: 1.0000 | Recall: 0.3636 | F1: 0.5333
+education: 12th, Count: 86
+Precision: 1.0000 | Recall: 0.1429 | F1: 0.2500
+education: 1st-4th, Count: 43
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+education: 5th-6th, Count: 67
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 7th-8th, Count: 104
+Precision: 0.6000 | Recall: 0.5000 | F1: 0.5455
+education: 9th, Count: 100
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: Assoc-acdm, Count: 205
+Precision: 0.7561 | Recall: 0.5849 | F1: 0.6596
+education: Assoc-voc, Count: 292
+Precision: 0.6667 | Recall: 0.5600 | F1: 0.6087
+education: Bachelors, Count: 1,068
+Precision: 0.7864 | Recall: 0.7522 | F1: 0.7689
+education: Doctorate, Count: 75
+Precision: 0.8548 | Recall: 0.9298 | F1: 0.8908
+education: HS-grad, Count: 2,105
+Precision: 0.6771 | Recall: 0.3746 | F1: 0.4824
+education: Masters, Count: 319
+Precision: 0.8034 | Recall: 0.8034 | F1: 0.8034
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 112
+Precision: 0.8471 | Recall: 0.9474 | F1: 0.8944
+education: Some-college, Count: 1,501
+Precision: 0.6481 | Recall: 0.5336 | F1: 0.5853
+marital-status: Divorced, Count: 869
+Precision: 0.7895 | Recall: 0.3448 | F1: 0.4800
+marital-status: Married-AF-spouse, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,996
+Precision: 0.7412 | Recall: 0.6679 | F1: 0.7026
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2222 | F1: 0.3333
+marital-status: Never-married, Count: 2,171
+Precision: 0.8718 | Recall: 0.3542 | F1: 0.5037
+marital-status: Separated, Count: 186
+Precision: 1.0000 | Recall: 0.3571 | F1: 0.5263
+marital-status: Widowed, Count: 187
+Precision: 1.0000 | Recall: 0.3077 | F1: 0.4706
+occupation: ?, Count: 346
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+occupation: Adm-clerical, Count: 791
+Precision: 0.6495 | Recall: 0.5431 | F1: 0.5915
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 820
+Precision: 0.5909 | Recall: 0.4358 | F1: 0.5016
+occupation: Exec-managerial, Count: 791
+Precision: 0.8072 | Recall: 0.7792 | F1: 0.7929
+occupation: Farming-fishing, Count: 203
+Precision: 0.7857 | Recall: 0.4783 | F1: 0.5946
+occupation: Handlers-cleaners, Count: 288
+Precision: 0.6667 | Recall: 0.1739 | F1: 0.2759
+occupation: Machine-op-inspct, Count: 427
+Precision: 0.6552 | Recall: 0.3725 | F1: 0.4750
+occupation: Other-service, Count: 647
+Precision: 0.8333 | Recall: 0.1562 | F1: 0.2632
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8128 | Recall: 0.7951 | F1: 0.8039
+occupation: Protective-serv, Count: 130
+Precision: 0.7353 | Recall: 0.5814 | F1: 0.6494
+occupation: Sales, Count: 739
+Precision: 0.6918 | Recall: 0.5288 | F1: 0.5994
+occupation: Tech-support, Count: 180
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+occupation: Transport-moving, Count: 306
+Precision: 0.6000 | Recall: 0.2679 | F1: 0.3704
+relationship: Husband, Count: 2,631
+Precision: 0.7392 | Recall: 0.6661 | F1: 0.7008
+relationship: Not-in-family, Count: 1,650
+Precision: 0.8451 | Recall: 0.3488 | F1: 0.4938
+relationship: Other-relative, Count: 184
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,082
+Precision: 1.0000 | Recall: 0.2941 | F1: 0.4545
+relationship: Unmarried, Count: 656
+Precision: 0.7857 | Recall: 0.3056 | F1: 0.4400
+relationship: Wife, Count: 309
+Precision: 0.7434 | Recall: 0.7107 | F1: 0.7267
+race: Amer-Indian-Eskimo, Count: 65
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+race: Asian-Pac-Islander, Count: 207
+Precision: 0.7273 | Recall: 0.5714 | F1: 0.6400
+race: Black, Count: 621
+Precision: 0.8333 | Recall: 0.5495 | F1: 0.6623
+race: Other, Count: 67
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+race: White, Count: 5,552
+Precision: 0.7450 | Recall: 0.6301 | F1: 0.6828
+sex: Female, Count: 2,130
+Precision: 0.7622 | Recall: 0.5851 | F1: 0.6620
+sex: Male, Count: 4,382
+Precision: 0.7451 | Recall: 0.6289 | F1: 0.6821
+native-country: ?, Count: 127
+Precision: 0.8065 | Recall: 0.6579 | F1: 0.7246
+native-country: Cambodia, Count: 2
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Canada, Count: 22
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+native-country: Columbia, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 16
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Dominican-Republic, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: El-Salvador, Count: 24
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 19
+Precision: 0.8750 | Recall: 0.7000 | F1: 0.7778
+native-country: France, Count: 5
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Germany, Count: 28
+Precision: 1.0000 | Recall: 0.5556 | F1: 0.7143
+native-country: Greece, Count: 7
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.7000 | Recall: 1.0000 | F1: 0.8235
+native-country: Iran, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Italy, Count: 14
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+native-country: Jamaica, Count: 19
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.7143 | F1: 0.8333
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 132
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+native-country: Nicaragua, Count: 9
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 38
+Precision: 0.5556 | Recall: 0.5000 | F1: 0.5263
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Puerto-Rico, Count: 19
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: South, Count: 18
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Taiwan, Count: 8
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: United-States, Count: 5,820
+Precision: 0.7438 | Recall: 0.6265 | F1: 0.6801
+native-country: Vietnam, Count: 14
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 377
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+workclass: Federal-gov, Count: 188
+Precision: 0.8136 | Recall: 0.6486 | F1: 0.7218
+workclass: Local-gov, Count: 415
+Precision: 0.7451 | Recall: 0.6129 | F1: 0.6726
+workclass: Never-worked, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,492
+Precision: 0.7312 | Recall: 0.6145 | F1: 0.6678
+workclass: Self-emp-inc, Count: 234
+Precision: 0.8222 | Recall: 0.8538 | F1: 0.8377
+workclass: Self-emp-not-inc, Count: 538
+Precision: 0.7607 | Recall: 0.5973 | F1: 0.6692
+workclass: State-gov, Count: 263
+Precision: 0.7705 | Recall: 0.6620 | F1: 0.7121
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 189
+Precision: 1.0000 | Recall: 0.0769 | F1: 0.1429
+education: 11th, Count: 233
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+education: 12th, Count: 87
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 1st-4th, Count: 31
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 123
+Precision: 0.5000 | Recall: 0.1429 | F1: 0.2222
+education: 9th, Count: 105
+Precision: 1.0000 | Recall: 0.1250 | F1: 0.2222
+education: Assoc-acdm, Count: 240
+Precision: 0.6275 | Recall: 0.5424 | F1: 0.5818
+education: Assoc-voc, Count: 283
+Precision: 0.6774 | Recall: 0.5600 | F1: 0.6131
+education: Bachelors, Count: 1,090
+Precision: 0.7551 | Recall: 0.7620 | F1: 0.7585
+education: Doctorate, Count: 93
+Precision: 0.8873 | Recall: 0.8514 | F1: 0.8690
+education: HS-grad, Count: 2,091
+Precision: 0.6590 | Recall: 0.4145 | F1: 0.5089
+education: Masters, Count: 355
+Precision: 0.8564 | Recall: 0.8350 | F1: 0.8456
+education: Preschool, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 111
+Precision: 0.8571 | Recall: 0.8780 | F1: 0.8675
+education: Some-college, Count: 1,413
+Precision: 0.6582 | Recall: 0.5265 | F1: 0.5850
+marital-status: Divorced, Count: 902
+Precision: 0.7857 | Recall: 0.3333 | F1: 0.4681
+marital-status: Married-AF-spouse, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,975
+Precision: 0.7359 | Recall: 0.6762 | F1: 0.7048
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+marital-status: Never-married, Count: 2,162
+Precision: 0.9020 | Recall: 0.4742 | F1: 0.6216
+marital-status: Separated, Count: 191
+Precision: 1.0000 | Recall: 0.2308 | F1: 0.3750
+marital-status: Widowed, Count: 200
+Precision: 0.8333 | Recall: 0.2778 | F1: 0.4167
+occupation: ?, Count: 380
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+occupation: Adm-clerical, Count: 730
+Precision: 0.6538 | Recall: 0.5543 | F1: 0.6000
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 816
+Precision: 0.6165 | Recall: 0.4339 | F1: 0.5093
+occupation: Exec-managerial, Count: 823
+Precision: 0.7805 | Recall: 0.8060 | F1: 0.7931
+occupation: Farming-fishing, Count: 201
+Precision: 0.8000 | Recall: 0.4615 | F1: 0.5854
+occupation: Handlers-cleaners, Count: 241
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+occupation: Machine-op-inspct, Count: 397
+Precision: 0.5294 | Recall: 0.3396 | F1: 0.4138
+occupation: Other-service, Count: 696
+Precision: 0.8571 | Recall: 0.2143 | F1: 0.3429
+occupation: Priv-house-serv, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 823
+Precision: 0.7943 | Recall: 0.7943 | F1: 0.7943
+occupation: Protective-serv, Count: 106
+Precision: 0.8276 | Recall: 0.5455 | F1: 0.6575
+occupation: Sales, Count: 767
+Precision: 0.7576 | Recall: 0.5787 | F1: 0.6562
+occupation: Tech-support, Count: 191
+Precision: 0.8140 | Recall: 0.5224 | F1: 0.6364
+occupation: Transport-moving, Count: 310
+Precision: 0.6341 | Recall: 0.3881 | F1: 0.4815
+relationship: Husband, Count: 2,626
+Precision: 0.7342 | Recall: 0.6802 | F1: 0.7062
+relationship: Not-in-family, Count: 1,666
+Precision: 0.8353 | Recall: 0.4152 | F1: 0.5547
+relationship: Other-relative, Count: 212
+Precision: 1.0000 | Recall: 0.1000 | F1: 0.1818
+relationship: Own-child, Count: 998
+Precision: 1.0000 | Recall: 0.4444 | F1: 0.6154
+relationship: Unmarried, Count: 707
+Precision: 0.9444 | Recall: 0.3269 | F1: 0.4857
+relationship: Wife, Count: 303
+Precision: 0.7419 | Recall: 0.6389 | F1: 0.6866
+race: Amer-Indian-Eskimo, Count: 53
+Precision: 0.3333 | Recall: 0.1667 | F1: 0.2222
+race: Asian-Pac-Islander, Count: 224
+Precision: 0.7333 | Recall: 0.6600 | F1: 0.6947
+race: Black, Count: 670
+Precision: 0.7895 | Recall: 0.5625 | F1: 0.6569
+race: Other, Count: 47
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+race: White, Count: 5,518
+Precision: 0.7453 | Recall: 0.6352 | F1: 0.6858
+sex: Female, Count: 2,196
+Precision: 0.7738 | Recall: 0.5221 | F1: 0.6235
+sex: Male, Count: 4,316
+Precision: 0.7412 | Recall: 0.6513 | F1: 0.6933
+native-country: ?, Count: 121
+Precision: 0.7692 | Recall: 0.7407 | F1: 0.7547
+native-country: Cambodia, Count: 4
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Canada, Count: 26
+Precision: 0.8571 | Recall: 0.8571 | F1: 0.8571
+native-country: China, Count: 17
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Columbia, Count: 17
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Cuba, Count: 12
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Dominican-Republic, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: England, Count: 19
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: France, Count: 6
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Germany, Count: 30
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Greece, Count: 10
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Guatemala, Count: 15
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.6667 | Recall: 0.7500 | F1: 0.7059
+native-country: Iran, Count: 11
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Ireland, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.3333 | Recall: 0.2500 | F1: 0.2857
+native-country: Jamaica, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Laos, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 125
+Precision: 0.8333 | Recall: 0.5556 | F1: 0.6667
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 47
+Precision: 0.8889 | Recall: 0.6154 | F1: 0.7273
+native-country: Poland, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 20
+Precision: 0.2500 | Recall: 0.3333 | F1: 0.2857
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,806
+Precision: 0.7448 | Recall: 0.6257 | F1: 0.6801
+native-country: Vietnam, Count: 15
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+workclass: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+workclass: Federal-gov, Count: 191
+Precision: 0.7571 | Recall: 0.7571 | F1: 0.7571
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6545 | F1: 0.6990
+workclass: Private, Count: 4,578
+Precision: 0.7447 | Recall: 0.6324 | F1: 0.6840
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7565 | Recall: 0.7373 | F1: 0.7468
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7339 | Recall: 0.5096 | F1: 0.6015
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6575 | F1: 0.7007
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.4286 | Recall: 0.2500 | F1: 0.3158
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.6923 | Recall: 0.5745 | F1: 0.6279
+education: Assoc-voc, Count: 273
+Precision: 0.6667 | Recall: 0.5397 | F1: 0.5965
+education: Bachelors, Count: 1,053
+Precision: 0.7564 | Recall: 0.7244 | F1: 0.7401
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6622 | Recall: 0.4261 | F1: 0.5185
+education: Masters, Count: 369
+Precision: 0.8309 | Recall: 0.8309 | F1: 0.8309
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8191 | Recall: 0.9167 | F1: 0.8652
+education: Some-college, Count: 1,485
+Precision: 0.6952 | Recall: 0.5271 | F1: 0.5996
+marital-status: Divorced, Count: 920
+Precision: 0.7826 | Recall: 0.3495 | F1: 0.4832
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7377 | Recall: 0.6816 | F1: 0.7085
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8182 | Recall: 0.4369 | F1: 0.5696
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+occupation: Adm-clerical, Count: 726
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6641 | Recall: 0.4696 | F1: 0.5502
+occupation: Exec-managerial, Count: 838
+Precision: 0.8011 | Recall: 0.7506 | F1: 0.7750
+occupation: Farming-fishing, Count: 193
+Precision: 0.6364 | Recall: 0.2500 | F1: 0.3590
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5938 | Recall: 0.4043 | F1: 0.4810
+occupation: Other-service, Count: 667
+Precision: 0.8571 | Recall: 0.2308 | F1: 0.3636
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7882 | Recall: 0.7500 | F1: 0.7686
+occupation: Protective-serv, Count: 136
+Precision: 0.6970 | Recall: 0.5476 | F1: 0.6133
+occupation: Sales, Count: 729
+Precision: 0.7333 | Recall: 0.6875 | F1: 0.7097
+occupation: Tech-support, Count: 189
+Precision: 0.6744 | Recall: 0.5686 | F1: 0.6170
+occupation: Transport-moving, Count: 317
+Precision: 0.6000 | Recall: 0.4219 | F1: 0.4954
+relationship: Husband, Count: 2,590
+Precision: 0.7401 | Recall: 0.6838 | F1: 0.7108
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8021 | Recall: 0.4096 | F1: 0.5423
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9231 | Recall: 0.2667 | F1: 0.4138
+relationship: Wife, Count: 322
+Precision: 0.7164 | Recall: 0.6713 | F1: 0.6931
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6250 | Recall: 0.5000 | F1: 0.5556
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7547 | Recall: 0.6452 | F1: 0.6957
+race: Black, Count: 599
+Precision: 0.7692 | Recall: 0.6154 | F1: 0.6838
+race: Other, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: White, Count: 5,595
+Precision: 0.7448 | Recall: 0.6317 | F1: 0.6836
+sex: Female, Count: 2,126
+Precision: 0.7267 | Recall: 0.5021 | F1: 0.5939
+sex: Male, Count: 4,387
+Precision: 0.7476 | Recall: 0.6532 | F1: 0.6972
+native-country: ?, Count: 125
+Precision: 0.7097 | Recall: 0.7097 | F1: 0.7097
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 22
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 0.8750 | F1: 0.9333
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8333 | Recall: 0.7692 | F1: 0.8000
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.7778 | Recall: 0.8750 | F1: 0.8235
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6250 | F1: 0.7692
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7448 | Recall: 0.6265 | F1: 0.6805
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 368
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+workclass: Federal-gov, Count: 186
+Precision: 0.7424 | Recall: 0.6447 | F1: 0.6901
+workclass: Local-gov, Count: 475
+Precision: 0.6912 | Recall: 0.6714 | F1: 0.6812
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,513
+Precision: 0.6974 | Recall: 0.6033 | F1: 0.6469
+workclass: Self-emp-inc, Count: 231
+Precision: 0.8319 | Recall: 0.7557 | F1: 0.7920
+workclass: Self-emp-not-inc, Count: 480
+Precision: 0.6598 | Recall: 0.5039 | F1: 0.5714
+workclass: State-gov, Count: 256
+Precision: 0.7188 | Recall: 0.7302 | F1: 0.7244
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 197
+Precision: 0.5000 | Recall: 0.0833 | F1: 0.1429
+education: 11th, Count: 235
+Precision: 0.8000 | Recall: 0.2857 | F1: 0.4211
+education: 12th, Count: 77
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 1st-4th, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 70
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 118
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 9th, Count: 84
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 225
+Precision: 0.6078 | Recall: 0.5741 | F1: 0.5905
+education: Assoc-voc, Count: 251
+Precision: 0.6341 | Recall: 0.4194 | F1: 0.5049
+education: Bachelors, Count: 1,071
+Precision: 0.7460 | Recall: 0.7600 | F1: 0.7529
+education: Doctorate, Count: 83
+Precision: 0.7812 | Recall: 0.8333 | F1: 0.8065
+education: HS-grad, Count: 2,157
+Precision: 0.5520 | Recall: 0.4220 | F1: 0.4783
+education: Masters, Count: 351
+Precision: 0.7949 | Recall: 0.7908 | F1: 0.7928
+education: Preschool, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: Prof-school, Count: 117
+Precision: 0.8696 | Recall: 0.9091 | F1: 0.8889
+education: Some-college, Count: 1,434
+Precision: 0.6761 | Recall: 0.4898 | F1: 0.5680
+marital-status: Divorced, Count: 853
+Precision: 0.8108 | Recall: 0.3448 | F1: 0.4839
+marital-status: Married-AF-spouse, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+marital-status: Married-civ-spouse, Count: 3,044
+Precision: 0.6967 | Recall: 0.6535 | F1: 0.6744
+marital-status: Married-spouse-absent, Count: 74
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+marital-status: Never-married, Count: 2,124
+Precision: 0.9070 | Recall: 0.3861 | F1: 0.5417
+marital-status: Separated, Count: 224
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+marital-status: Widowed, Count: 191
+Precision: 0.8889 | Recall: 0.4706 | F1: 0.6154
+occupation: ?, Count: 369
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+occupation: Adm-clerical, Count: 768
+Precision: 0.6364 | Recall: 0.4949 | F1: 0.5568
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 793
+Precision: 0.6000 | Recall: 0.4693 | F1: 0.5266
+occupation: Exec-managerial, Count: 806
+Precision: 0.7886 | Recall: 0.7500 | F1: 0.7688
+occupation: Farming-fishing, Count: 196
+Precision: 0.6000 | Recall: 0.3750 | F1: 0.4615
+occupation: Handlers-cleaners, Count: 271
+Precision: 0.6667 | Recall: 0.1053 | F1: 0.1818
+occupation: Machine-op-inspct, Count: 392
+Precision: 0.5789 | Recall: 0.2245 | F1: 0.3235
+occupation: Other-service, Count: 659
+Precision: 0.6667 | Recall: 0.1538 | F1: 0.2500
+occupation: Priv-house-serv, Count: 36
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 855
+Precision: 0.7454 | Recall: 0.7358 | F1: 0.7405
+occupation: Protective-serv, Count: 148
+Precision: 0.6170 | Recall: 0.6444 | F1: 0.6304
+occupation: Sales, Count: 686
+Precision: 0.6570 | Recall: 0.6175 | F1: 0.6366
+occupation: Tech-support, Count: 178
+Precision: 0.7037 | Recall: 0.7917 | F1: 0.7451
+occupation: Transport-moving, Count: 353
+Precision: 0.6122 | Recall: 0.3947 | F1: 0.4800
+relationship: Husband, Count: 2,652
+Precision: 0.7039 | Recall: 0.6520 | F1: 0.6770
+relationship: Not-in-family, Count: 1,639
+Precision: 0.8659 | Recall: 0.4176 | F1: 0.5635
+relationship: Other-relative, Count: 199
+Precision: 0.5000 | Recall: 0.1667 | F1: 0.2500
+relationship: Own-child, Count: 1,000
+Precision: 0.7500 | Recall: 0.2143 | F1: 0.3333
+relationship: Unmarried, Count: 673
+Precision: 0.7857 | Recall: 0.3143 | F1: 0.4490
+relationship: Wife, Count: 349
+Precision: 0.6564 | Recall: 0.6859 | F1: 0.6708
+race: Amer-Indian-Eskimo, Count: 67
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 205
+Precision: 0.7561 | Recall: 0.5536 | F1: 0.6392
+race: Black, Count: 600
+Precision: 0.6078 | Recall: 0.4366 | F1: 0.5082
+race: Other, Count: 52
+Precision: 0.6667 | Recall: 0.4000 | F1: 0.5000
+race: White, Count: 5,588
+Precision: 0.7102 | Recall: 0.6295 | F1: 0.6674
+sex: Female, Count: 2,195
+Precision: 0.6869 | Recall: 0.5738 | F1: 0.6253
+sex: Male, Count: 4,317
+Precision: 0.7124 | Recall: 0.6241 | F1: 0.6653
+native-country: ?, Count: 106
+Precision: 0.8182 | Recall: 0.6000 | F1: 0.6923
+native-country: Cambodia, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 26
+Precision: 0.7500 | Recall: 0.3750 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+native-country: Columbia, Count: 12
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 27
+Precision: 0.8750 | Recall: 0.7778 | F1: 0.8235
+native-country: Dominican-Republic, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: El-Salvador, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 20
+Precision: 0.7500 | Recall: 0.4286 | F1: 0.5455
+native-country: France, Count: 6
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Germany, Count: 20
+Precision: 0.6667 | Recall: 0.2857 | F1: 0.4000
+native-country: Greece, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Guatemala, Count: 10
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 12
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 2
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: India, Count: 24
+Precision: 0.8571 | Recall: 0.6667 | F1: 0.7500
+native-country: Iran, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Ireland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 11
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Jamaica, Count: 21
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 9
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 120
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Nicaragua, Count: 6
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 41
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+native-country: Poland, Count: 13
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Puerto-Rico, Count: 19
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Scotland, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: South, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: United-States, Count: 5,856
+Precision: 0.7068 | Recall: 0.6225 | F1: 0.6619
+native-country: Vietnam, Count: 13
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+workclass: ?, Count: 357
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+workclass: Federal-gov, Count: 195
+Precision: 0.7179 | Recall: 0.7887 | F1: 0.7517
+workclass: Local-gov, Count: 395
+Precision: 0.6939 | Recall: 0.5812 | F1: 0.6326
+workclass: Never-worked, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,566
+Precision: 0.7378 | Recall: 0.6123 | F1: 0.6692
+workclass: Self-emp-inc, Count: 225
+Precision: 0.7826 | Recall: 0.7500 | F1: 0.7660
+workclass: Self-emp-not-inc, Count: 514
+Precision: 0.7475 | Recall: 0.5000 | F1: 0.5992
+workclass: State-gov, Count: 255
+Precision: 0.7273 | Recall: 0.7089 | F1: 0.7179
+workclass: Without-pay, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 180
+Precision: 0.7500 | Recall: 0.5000 | F1: 0.6000
+education: 11th, Count: 242
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: 12th, Count: 85
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 1st-4th, Count: 42
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 72
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 160
+Precision: 0.5000 | Recall: 0.1000 | F1: 0.1667
+education: 9th, Count: 110
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 199
+Precision: 0.6889 | Recall: 0.5962 | F1: 0.6392
+education: Assoc-voc, Count: 283
+Precision: 0.8333 | Recall: 0.5233 | F1: 0.6429
+education: Bachelors, Count: 1,073
+Precision: 0.7332 | Recall: 0.7528 | F1: 0.7429
+education: Doctorate, Count: 85
+Precision: 0.7536 | Recall: 0.8966 | F1: 0.8189
+education: HS-grad, Count: 2,063
+Precision: 0.5864 | Recall: 0.3601 | F1: 0.4462
+education: Masters, Count: 329
+Precision: 0.8218 | Recall: 0.8034 | F1: 0.8125
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 120
+Precision: 0.9184 | Recall: 0.9677 | F1: 0.9424
+education: Some-college, Count: 1,458
+Precision: 0.6887 | Recall: 0.5069 | F1: 0.5840
+marital-status: Divorced, Count: 899
+Precision: 0.6512 | Recall: 0.3218 | F1: 0.4308
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 3,011
+Precision: 0.7336 | Recall: 0.6603 | F1: 0.6950
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+marital-status: Never-married, Count: 2,100
+Precision: 0.7800 | Recall: 0.4149 | F1: 0.5417
+marital-status: Separated, Count: 215
+Precision: 1.0000 | Recall: 0.4167 | F1: 0.5882
+marital-status: Widowed, Count: 207
+Precision: 1.0000 | Recall: 0.2222 | F1: 0.3636
+occupation: ?, Count: 359
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+occupation: Adm-clerical, Count: 755
+Precision: 0.6076 | Recall: 0.4615 | F1: 0.5246
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 849
+Precision: 0.6718 | Recall: 0.4378 | F1: 0.5301
+occupation: Exec-managerial, Count: 808
+Precision: 0.7836 | Recall: 0.7467 | F1: 0.7647
+occupation: Farming-fishing, Count: 201
+Precision: 0.7143 | Recall: 0.2273 | F1: 0.3448
+occupation: Handlers-cleaners, Count: 297
+Precision: 0.6667 | Recall: 0.2353 | F1: 0.3478
+occupation: Machine-op-inspct, Count: 408
+Precision: 0.6316 | Recall: 0.2400 | F1: 0.3478
+occupation: Other-service, Count: 626
+Precision: 0.7500 | Recall: 0.1200 | F1: 0.2069
+occupation: Priv-house-serv, Count: 32
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8000 | Recall: 0.7890 | F1: 0.7945
+occupation: Protective-serv, Count: 129
+Precision: 0.5714 | Recall: 0.5405 | F1: 0.5556
+occupation: Sales, Count: 729
+Precision: 0.6989 | Recall: 0.6468 | F1: 0.6718
+occupation: Tech-support, Count: 190
+Precision: 0.6721 | Recall: 0.6721 | F1: 0.6721
+occupation: Transport-moving, Count: 311
+Precision: 0.7059 | Recall: 0.4211 | F1: 0.5275
+relationship: Husband, Count: 2,694
+Precision: 0.7378 | Recall: 0.6656 | F1: 0.6998
+relationship: Not-in-family, Count: 1,648
+Precision: 0.7342 | Recall: 0.3742 | F1: 0.4957
+relationship: Other-relative, Count: 208
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+relationship: Own-child, Count: 969
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Unmarried, Count: 708
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Wife, Count: 285
+Precision: 0.7031 | Recall: 0.6294 | F1: 0.6642
+race: Amer-Indian-Eskimo, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 210
+Precision: 0.6383 | Recall: 0.5769 | F1: 0.6061
+race: Black, Count: 634
+Precision: 0.7581 | Recall: 0.5875 | F1: 0.6620
+race: Other, Count: 50
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+race: White, Count: 5,563
+Precision: 0.7378 | Recall: 0.6217 | F1: 0.6748
+sex: Female, Count: 2,124
+Precision: 0.7099 | Recall: 0.5251 | F1: 0.6037
+sex: Male, Count: 4,388
+Precision: 0.7381 | Recall: 0.6331 | F1: 0.6816
+native-country: ?, Count: 104
+Precision: 0.5714 | Recall: 0.6000 | F1: 0.5854
+native-country: Cambodia, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 25
+Precision: 0.7143 | Recall: 0.5556 | F1: 0.6250
+native-country: China, Count: 10
+Precision: 0.3333 | Recall: 1.0000 | F1: 0.5000
+native-country: Columbia, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 21
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Dominican-Republic, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 25
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: England, Count: 18
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: France, Count: 7
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Germany, Count: 27
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+native-country: Greece, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Holand-Netherlands, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 15
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: Iran, Count: 9
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Ireland, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 20
+Precision: 1.0000 | Recall: 0.5714 | F1: 0.7273
+native-country: Jamaica, Count: 14
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 16
+Precision: 0.5000 | Recall: 0.6667 | F1: 0.5714
+native-country: Laos, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Mexico, Count: 152
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+native-country: Nicaragua, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Outlying-US(Guam-USVI-etc), Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 37
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Poland, Count: 8
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Portugal, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 12
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Taiwan, Count: 14
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,818
+Precision: 0.7428 | Recall: 0.6205 | F1: 0.6762
+native-country: Vietnam, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 345
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+workclass: Federal-gov, Count: 200
+Precision: 0.7973 | Recall: 0.7375 | F1: 0.7662
+workclass: Local-gov, Count: 421
+Precision: 0.7434 | Recall: 0.6667 | F1: 0.7029
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,547
+Precision: 0.7450 | Recall: 0.5990 | F1: 0.6641
+workclass: Self-emp-inc, Count: 214
+Precision: 0.7769 | Recall: 0.8211 | F1: 0.7984
+workclass: Self-emp-not-inc, Count: 511
+Precision: 0.6667 | Recall: 0.5035 | F1: 0.5737
+workclass: State-gov, Count: 270
+Precision: 0.8364 | Recall: 0.6866 | F1: 0.7541
+workclass: Without-pay, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 10th, Count: 184
+Precision: 0.5000 | Recall: 0.1538 | F1: 0.2353
+education: 11th, Count: 240
+Precision: 1.0000 | Recall: 0.3636 | F1: 0.5333
+education: 12th, Count: 86
+Precision: 1.0000 | Recall: 0.1429 | F1: 0.2500
+education: 1st-4th, Count: 43
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+education: 5th-6th, Count: 67
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 7th-8th, Count: 104
+Precision: 0.6000 | Recall: 0.5000 | F1: 0.5455
+education: 9th, Count: 100
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: Assoc-acdm, Count: 205
+Precision: 0.7561 | Recall: 0.5849 | F1: 0.6596
+education: Assoc-voc, Count: 292
+Precision: 0.6667 | Recall: 0.5600 | F1: 0.6087
+education: Bachelors, Count: 1,068
+Precision: 0.7864 | Recall: 0.7522 | F1: 0.7689
+education: Doctorate, Count: 75
+Precision: 0.8548 | Recall: 0.9298 | F1: 0.8908
+education: HS-grad, Count: 2,105
+Precision: 0.6771 | Recall: 0.3746 | F1: 0.4824
+education: Masters, Count: 319
+Precision: 0.8034 | Recall: 0.8034 | F1: 0.8034
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 112
+Precision: 0.8471 | Recall: 0.9474 | F1: 0.8944
+education: Some-college, Count: 1,501
+Precision: 0.6481 | Recall: 0.5336 | F1: 0.5853
+marital-status: Divorced, Count: 869
+Precision: 0.7895 | Recall: 0.3448 | F1: 0.4800
+marital-status: Married-AF-spouse, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,996
+Precision: 0.7412 | Recall: 0.6679 | F1: 0.7026
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2222 | F1: 0.3333
+marital-status: Never-married, Count: 2,171
+Precision: 0.8718 | Recall: 0.3542 | F1: 0.5037
+marital-status: Separated, Count: 186
+Precision: 1.0000 | Recall: 0.3571 | F1: 0.5263
+marital-status: Widowed, Count: 187
+Precision: 1.0000 | Recall: 0.3077 | F1: 0.4706
+occupation: ?, Count: 346
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+occupation: Adm-clerical, Count: 791
+Precision: 0.6495 | Recall: 0.5431 | F1: 0.5915
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 820
+Precision: 0.5909 | Recall: 0.4358 | F1: 0.5016
+occupation: Exec-managerial, Count: 791
+Precision: 0.8072 | Recall: 0.7792 | F1: 0.7929
+occupation: Farming-fishing, Count: 203
+Precision: 0.7857 | Recall: 0.4783 | F1: 0.5946
+occupation: Handlers-cleaners, Count: 288
+Precision: 0.6667 | Recall: 0.1739 | F1: 0.2759
+occupation: Machine-op-inspct, Count: 427
+Precision: 0.6552 | Recall: 0.3725 | F1: 0.4750
+occupation: Other-service, Count: 647
+Precision: 0.8333 | Recall: 0.1562 | F1: 0.2632
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8128 | Recall: 0.7951 | F1: 0.8039
+occupation: Protective-serv, Count: 130
+Precision: 0.7353 | Recall: 0.5814 | F1: 0.6494
+occupation: Sales, Count: 739
+Precision: 0.6918 | Recall: 0.5288 | F1: 0.5994
+occupation: Tech-support, Count: 180
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+occupation: Transport-moving, Count: 306
+Precision: 0.6000 | Recall: 0.2679 | F1: 0.3704
+relationship: Husband, Count: 2,631
+Precision: 0.7392 | Recall: 0.6661 | F1: 0.7008
+relationship: Not-in-family, Count: 1,650
+Precision: 0.8451 | Recall: 0.3488 | F1: 0.4938
+relationship: Other-relative, Count: 184
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,082
+Precision: 1.0000 | Recall: 0.2941 | F1: 0.4545
+relationship: Unmarried, Count: 656
+Precision: 0.7857 | Recall: 0.3056 | F1: 0.4400
+relationship: Wife, Count: 309
+Precision: 0.7434 | Recall: 0.7107 | F1: 0.7267
+race: Amer-Indian-Eskimo, Count: 65
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+race: Asian-Pac-Islander, Count: 207
+Precision: 0.7273 | Recall: 0.5714 | F1: 0.6400
+race: Black, Count: 621
+Precision: 0.8333 | Recall: 0.5495 | F1: 0.6623
+race: Other, Count: 67
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+race: White, Count: 5,552
+Precision: 0.7450 | Recall: 0.6301 | F1: 0.6828
+sex: Female, Count: 2,130
+Precision: 0.7622 | Recall: 0.5851 | F1: 0.6620
+sex: Male, Count: 4,382
+Precision: 0.7451 | Recall: 0.6289 | F1: 0.6821
+native-country: ?, Count: 127
+Precision: 0.8065 | Recall: 0.6579 | F1: 0.7246
+native-country: Cambodia, Count: 2
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Canada, Count: 22
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+native-country: Columbia, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 16
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Dominican-Republic, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: El-Salvador, Count: 24
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 19
+Precision: 0.8750 | Recall: 0.7000 | F1: 0.7778
+native-country: France, Count: 5
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Germany, Count: 28
+Precision: 1.0000 | Recall: 0.5556 | F1: 0.7143
+native-country: Greece, Count: 7
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.7000 | Recall: 1.0000 | F1: 0.8235
+native-country: Iran, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Italy, Count: 14
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+native-country: Jamaica, Count: 19
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.7143 | F1: 0.8333
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 132
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+native-country: Nicaragua, Count: 9
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 38
+Precision: 0.5556 | Recall: 0.5000 | F1: 0.5263
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Puerto-Rico, Count: 19
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: South, Count: 18
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Taiwan, Count: 8
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: United-States, Count: 5,820
+Precision: 0.7438 | Recall: 0.6265 | F1: 0.6801
+native-country: Vietnam, Count: 14
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 377
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+workclass: Federal-gov, Count: 188
+Precision: 0.8136 | Recall: 0.6486 | F1: 0.7218
+workclass: Local-gov, Count: 415
+Precision: 0.7451 | Recall: 0.6129 | F1: 0.6726
+workclass: Never-worked, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,492
+Precision: 0.7312 | Recall: 0.6145 | F1: 0.6678
+workclass: Self-emp-inc, Count: 234
+Precision: 0.8222 | Recall: 0.8538 | F1: 0.8377
+workclass: Self-emp-not-inc, Count: 538
+Precision: 0.7607 | Recall: 0.5973 | F1: 0.6692
+workclass: State-gov, Count: 263
+Precision: 0.7705 | Recall: 0.6620 | F1: 0.7121
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 189
+Precision: 1.0000 | Recall: 0.0769 | F1: 0.1429
+education: 11th, Count: 233
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+education: 12th, Count: 87
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 1st-4th, Count: 31
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 123
+Precision: 0.5000 | Recall: 0.1429 | F1: 0.2222
+education: 9th, Count: 105
+Precision: 1.0000 | Recall: 0.1250 | F1: 0.2222
+education: Assoc-acdm, Count: 240
+Precision: 0.6275 | Recall: 0.5424 | F1: 0.5818
+education: Assoc-voc, Count: 283
+Precision: 0.6774 | Recall: 0.5600 | F1: 0.6131
+education: Bachelors, Count: 1,090
+Precision: 0.7551 | Recall: 0.7620 | F1: 0.7585
+education: Doctorate, Count: 93
+Precision: 0.8873 | Recall: 0.8514 | F1: 0.8690
+education: HS-grad, Count: 2,091
+Precision: 0.6590 | Recall: 0.4145 | F1: 0.5089
+education: Masters, Count: 355
+Precision: 0.8564 | Recall: 0.8350 | F1: 0.8456
+education: Preschool, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 111
+Precision: 0.8571 | Recall: 0.8780 | F1: 0.8675
+education: Some-college, Count: 1,413
+Precision: 0.6582 | Recall: 0.5265 | F1: 0.5850
+marital-status: Divorced, Count: 902
+Precision: 0.7857 | Recall: 0.3333 | F1: 0.4681
+marital-status: Married-AF-spouse, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,975
+Precision: 0.7359 | Recall: 0.6762 | F1: 0.7048
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+marital-status: Never-married, Count: 2,162
+Precision: 0.9020 | Recall: 0.4742 | F1: 0.6216
+marital-status: Separated, Count: 191
+Precision: 1.0000 | Recall: 0.2308 | F1: 0.3750
+marital-status: Widowed, Count: 200
+Precision: 0.8333 | Recall: 0.2778 | F1: 0.4167
+occupation: ?, Count: 380
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+occupation: Adm-clerical, Count: 730
+Precision: 0.6538 | Recall: 0.5543 | F1: 0.6000
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 816
+Precision: 0.6165 | Recall: 0.4339 | F1: 0.5093
+occupation: Exec-managerial, Count: 823
+Precision: 0.7805 | Recall: 0.8060 | F1: 0.7931
+occupation: Farming-fishing, Count: 201
+Precision: 0.8000 | Recall: 0.4615 | F1: 0.5854
+occupation: Handlers-cleaners, Count: 241
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+occupation: Machine-op-inspct, Count: 397
+Precision: 0.5294 | Recall: 0.3396 | F1: 0.4138
+occupation: Other-service, Count: 696
+Precision: 0.8571 | Recall: 0.2143 | F1: 0.3429
+occupation: Priv-house-serv, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 823
+Precision: 0.7943 | Recall: 0.7943 | F1: 0.7943
+occupation: Protective-serv, Count: 106
+Precision: 0.8276 | Recall: 0.5455 | F1: 0.6575
+occupation: Sales, Count: 767
+Precision: 0.7576 | Recall: 0.5787 | F1: 0.6562
+occupation: Tech-support, Count: 191
+Precision: 0.8140 | Recall: 0.5224 | F1: 0.6364
+occupation: Transport-moving, Count: 310
+Precision: 0.6341 | Recall: 0.3881 | F1: 0.4815
+relationship: Husband, Count: 2,626
+Precision: 0.7342 | Recall: 0.6802 | F1: 0.7062
+relationship: Not-in-family, Count: 1,666
+Precision: 0.8353 | Recall: 0.4152 | F1: 0.5547
+relationship: Other-relative, Count: 212
+Precision: 1.0000 | Recall: 0.1000 | F1: 0.1818
+relationship: Own-child, Count: 998
+Precision: 1.0000 | Recall: 0.4444 | F1: 0.6154
+relationship: Unmarried, Count: 707
+Precision: 0.9444 | Recall: 0.3269 | F1: 0.4857
+relationship: Wife, Count: 303
+Precision: 0.7419 | Recall: 0.6389 | F1: 0.6866
+race: Amer-Indian-Eskimo, Count: 53
+Precision: 0.3333 | Recall: 0.1667 | F1: 0.2222
+race: Asian-Pac-Islander, Count: 224
+Precision: 0.7333 | Recall: 0.6600 | F1: 0.6947
+race: Black, Count: 670
+Precision: 0.7895 | Recall: 0.5625 | F1: 0.6569
+race: Other, Count: 47
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+race: White, Count: 5,518
+Precision: 0.7453 | Recall: 0.6352 | F1: 0.6858
+sex: Female, Count: 2,196
+Precision: 0.7738 | Recall: 0.5221 | F1: 0.6235
+sex: Male, Count: 4,316
+Precision: 0.7412 | Recall: 0.6513 | F1: 0.6933
+native-country: ?, Count: 121
+Precision: 0.7692 | Recall: 0.7407 | F1: 0.7547
+native-country: Cambodia, Count: 4
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Canada, Count: 26
+Precision: 0.8571 | Recall: 0.8571 | F1: 0.8571
+native-country: China, Count: 17
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Columbia, Count: 17
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Cuba, Count: 12
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Dominican-Republic, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: England, Count: 19
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: France, Count: 6
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Germany, Count: 30
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Greece, Count: 10
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Guatemala, Count: 15
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.6667 | Recall: 0.7500 | F1: 0.7059
+native-country: Iran, Count: 11
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Ireland, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.3333 | Recall: 0.2500 | F1: 0.2857
+native-country: Jamaica, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Laos, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 125
+Precision: 0.8333 | Recall: 0.5556 | F1: 0.6667
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 47
+Precision: 0.8889 | Recall: 0.6154 | F1: 0.7273
+native-country: Poland, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 20
+Precision: 0.2500 | Recall: 0.3333 | F1: 0.2857
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,806
+Precision: 0.7448 | Recall: 0.6257 | F1: 0.6801
+native-country: Vietnam, Count: 15
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+workclass: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+workclass: Federal-gov, Count: 191
+Precision: 0.7571 | Recall: 0.7571 | F1: 0.7571
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6545 | F1: 0.6990
+workclass: Private, Count: 4,578
+Precision: 0.7447 | Recall: 0.6324 | F1: 0.6840
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7565 | Recall: 0.7373 | F1: 0.7468
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7339 | Recall: 0.5096 | F1: 0.6015
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6575 | F1: 0.7007
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.4286 | Recall: 0.2500 | F1: 0.3158
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.6923 | Recall: 0.5745 | F1: 0.6279
+education: Assoc-voc, Count: 273
+Precision: 0.6667 | Recall: 0.5397 | F1: 0.5965
+education: Bachelors, Count: 1,053
+Precision: 0.7564 | Recall: 0.7244 | F1: 0.7401
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6622 | Recall: 0.4261 | F1: 0.5185
+education: Masters, Count: 369
+Precision: 0.8309 | Recall: 0.8309 | F1: 0.8309
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8191 | Recall: 0.9167 | F1: 0.8652
+education: Some-college, Count: 1,485
+Precision: 0.6952 | Recall: 0.5271 | F1: 0.5996
+marital-status: Divorced, Count: 920
+Precision: 0.7826 | Recall: 0.3495 | F1: 0.4832
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7377 | Recall: 0.6816 | F1: 0.7085
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8182 | Recall: 0.4369 | F1: 0.5696
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+occupation: Adm-clerical, Count: 726
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6641 | Recall: 0.4696 | F1: 0.5502
+occupation: Exec-managerial, Count: 838
+Precision: 0.8011 | Recall: 0.7506 | F1: 0.7750
+occupation: Farming-fishing, Count: 193
+Precision: 0.6364 | Recall: 0.2500 | F1: 0.3590
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5938 | Recall: 0.4043 | F1: 0.4810
+occupation: Other-service, Count: 667
+Precision: 0.8571 | Recall: 0.2308 | F1: 0.3636
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7882 | Recall: 0.7500 | F1: 0.7686
+occupation: Protective-serv, Count: 136
+Precision: 0.6970 | Recall: 0.5476 | F1: 0.6133
+occupation: Sales, Count: 729
+Precision: 0.7333 | Recall: 0.6875 | F1: 0.7097
+occupation: Tech-support, Count: 189
+Precision: 0.6744 | Recall: 0.5686 | F1: 0.6170
+occupation: Transport-moving, Count: 317
+Precision: 0.6000 | Recall: 0.4219 | F1: 0.4954
+relationship: Husband, Count: 2,590
+Precision: 0.7401 | Recall: 0.6838 | F1: 0.7108
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8021 | Recall: 0.4096 | F1: 0.5423
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9231 | Recall: 0.2667 | F1: 0.4138
+relationship: Wife, Count: 322
+Precision: 0.7164 | Recall: 0.6713 | F1: 0.6931
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6250 | Recall: 0.5000 | F1: 0.5556
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7547 | Recall: 0.6452 | F1: 0.6957
+race: Black, Count: 599
+Precision: 0.7692 | Recall: 0.6154 | F1: 0.6838
+race: Other, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: White, Count: 5,595
+Precision: 0.7448 | Recall: 0.6317 | F1: 0.6836
+sex: Female, Count: 2,126
+Precision: 0.7267 | Recall: 0.5021 | F1: 0.5939
+sex: Male, Count: 4,387
+Precision: 0.7476 | Recall: 0.6532 | F1: 0.6972
+native-country: ?, Count: 125
+Precision: 0.7097 | Recall: 0.7097 | F1: 0.7097
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 22
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 0.8750 | F1: 0.9333
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8333 | Recall: 0.7692 | F1: 0.8000
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.7778 | Recall: 0.8750 | F1: 0.8235
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6250 | F1: 0.7692
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7448 | Recall: 0.6265 | F1: 0.6805
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 368
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+workclass: Federal-gov, Count: 186
+Precision: 0.7424 | Recall: 0.6447 | F1: 0.6901
+workclass: Local-gov, Count: 475
+Precision: 0.6912 | Recall: 0.6714 | F1: 0.6812
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,513
+Precision: 0.6974 | Recall: 0.6033 | F1: 0.6469
+workclass: Self-emp-inc, Count: 231
+Precision: 0.8319 | Recall: 0.7557 | F1: 0.7920
+workclass: Self-emp-not-inc, Count: 480
+Precision: 0.6598 | Recall: 0.5039 | F1: 0.5714
+workclass: State-gov, Count: 256
+Precision: 0.7188 | Recall: 0.7302 | F1: 0.7244
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 197
+Precision: 0.5000 | Recall: 0.0833 | F1: 0.1429
+education: 11th, Count: 235
+Precision: 0.8000 | Recall: 0.2857 | F1: 0.4211
+education: 12th, Count: 77
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 1st-4th, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 70
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 118
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 9th, Count: 84
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 225
+Precision: 0.6078 | Recall: 0.5741 | F1: 0.5905
+education: Assoc-voc, Count: 251
+Precision: 0.6341 | Recall: 0.4194 | F1: 0.5049
+education: Bachelors, Count: 1,071
+Precision: 0.7460 | Recall: 0.7600 | F1: 0.7529
+education: Doctorate, Count: 83
+Precision: 0.7812 | Recall: 0.8333 | F1: 0.8065
+education: HS-grad, Count: 2,157
+Precision: 0.5520 | Recall: 0.4220 | F1: 0.4783
+education: Masters, Count: 351
+Precision: 0.7949 | Recall: 0.7908 | F1: 0.7928
+education: Preschool, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: Prof-school, Count: 117
+Precision: 0.8696 | Recall: 0.9091 | F1: 0.8889
+education: Some-college, Count: 1,434
+Precision: 0.6761 | Recall: 0.4898 | F1: 0.5680
+marital-status: Divorced, Count: 853
+Precision: 0.8108 | Recall: 0.3448 | F1: 0.4839
+marital-status: Married-AF-spouse, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+marital-status: Married-civ-spouse, Count: 3,044
+Precision: 0.6967 | Recall: 0.6535 | F1: 0.6744
+marital-status: Married-spouse-absent, Count: 74
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+marital-status: Never-married, Count: 2,124
+Precision: 0.9070 | Recall: 0.3861 | F1: 0.5417
+marital-status: Separated, Count: 224
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+marital-status: Widowed, Count: 191
+Precision: 0.8889 | Recall: 0.4706 | F1: 0.6154
+occupation: ?, Count: 369
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+occupation: Adm-clerical, Count: 768
+Precision: 0.6364 | Recall: 0.4949 | F1: 0.5568
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 793
+Precision: 0.6000 | Recall: 0.4693 | F1: 0.5266
+occupation: Exec-managerial, Count: 806
+Precision: 0.7886 | Recall: 0.7500 | F1: 0.7688
+occupation: Farming-fishing, Count: 196
+Precision: 0.6000 | Recall: 0.3750 | F1: 0.4615
+occupation: Handlers-cleaners, Count: 271
+Precision: 0.6667 | Recall: 0.1053 | F1: 0.1818
+occupation: Machine-op-inspct, Count: 392
+Precision: 0.5789 | Recall: 0.2245 | F1: 0.3235
+occupation: Other-service, Count: 659
+Precision: 0.6667 | Recall: 0.1538 | F1: 0.2500
+occupation: Priv-house-serv, Count: 36
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 855
+Precision: 0.7454 | Recall: 0.7358 | F1: 0.7405
+occupation: Protective-serv, Count: 148
+Precision: 0.6170 | Recall: 0.6444 | F1: 0.6304
+occupation: Sales, Count: 686
+Precision: 0.6570 | Recall: 0.6175 | F1: 0.6366
+occupation: Tech-support, Count: 178
+Precision: 0.7037 | Recall: 0.7917 | F1: 0.7451
+occupation: Transport-moving, Count: 353
+Precision: 0.6122 | Recall: 0.3947 | F1: 0.4800
+relationship: Husband, Count: 2,652
+Precision: 0.7039 | Recall: 0.6520 | F1: 0.6770
+relationship: Not-in-family, Count: 1,639
+Precision: 0.8659 | Recall: 0.4176 | F1: 0.5635
+relationship: Other-relative, Count: 199
+Precision: 0.5000 | Recall: 0.1667 | F1: 0.2500
+relationship: Own-child, Count: 1,000
+Precision: 0.7500 | Recall: 0.2143 | F1: 0.3333
+relationship: Unmarried, Count: 673
+Precision: 0.7857 | Recall: 0.3143 | F1: 0.4490
+relationship: Wife, Count: 349
+Precision: 0.6564 | Recall: 0.6859 | F1: 0.6708
+race: Amer-Indian-Eskimo, Count: 67
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 205
+Precision: 0.7561 | Recall: 0.5536 | F1: 0.6392
+race: Black, Count: 600
+Precision: 0.6078 | Recall: 0.4366 | F1: 0.5082
+race: Other, Count: 52
+Precision: 0.6667 | Recall: 0.4000 | F1: 0.5000
+race: White, Count: 5,588
+Precision: 0.7102 | Recall: 0.6295 | F1: 0.6674
+sex: Female, Count: 2,195
+Precision: 0.6869 | Recall: 0.5738 | F1: 0.6253
+sex: Male, Count: 4,317
+Precision: 0.7124 | Recall: 0.6241 | F1: 0.6653
+native-country: ?, Count: 106
+Precision: 0.8182 | Recall: 0.6000 | F1: 0.6923
+native-country: Cambodia, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 26
+Precision: 0.7500 | Recall: 0.3750 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+native-country: Columbia, Count: 12
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 27
+Precision: 0.8750 | Recall: 0.7778 | F1: 0.8235
+native-country: Dominican-Republic, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: El-Salvador, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 20
+Precision: 0.7500 | Recall: 0.4286 | F1: 0.5455
+native-country: France, Count: 6
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Germany, Count: 20
+Precision: 0.6667 | Recall: 0.2857 | F1: 0.4000
+native-country: Greece, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Guatemala, Count: 10
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 12
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 2
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: India, Count: 24
+Precision: 0.8571 | Recall: 0.6667 | F1: 0.7500
+native-country: Iran, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Ireland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 11
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Jamaica, Count: 21
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 9
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 120
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Nicaragua, Count: 6
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 41
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+native-country: Poland, Count: 13
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Puerto-Rico, Count: 19
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Scotland, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: South, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: United-States, Count: 5,856
+Precision: 0.7068 | Recall: 0.6225 | F1: 0.6619
+native-country: Vietnam, Count: 13
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+workclass: ?, Count: 357
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+workclass: Federal-gov, Count: 195
+Precision: 0.7179 | Recall: 0.7887 | F1: 0.7517
+workclass: Local-gov, Count: 395
+Precision: 0.6939 | Recall: 0.5812 | F1: 0.6326
+workclass: Never-worked, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,566
+Precision: 0.7378 | Recall: 0.6123 | F1: 0.6692
+workclass: Self-emp-inc, Count: 225
+Precision: 0.7826 | Recall: 0.7500 | F1: 0.7660
+workclass: Self-emp-not-inc, Count: 514
+Precision: 0.7475 | Recall: 0.5000 | F1: 0.5992
+workclass: State-gov, Count: 255
+Precision: 0.7273 | Recall: 0.7089 | F1: 0.7179
+workclass: Without-pay, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 180
+Precision: 0.7500 | Recall: 0.5000 | F1: 0.6000
+education: 11th, Count: 242
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: 12th, Count: 85
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 1st-4th, Count: 42
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 72
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 160
+Precision: 0.5000 | Recall: 0.1000 | F1: 0.1667
+education: 9th, Count: 110
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 199
+Precision: 0.6889 | Recall: 0.5962 | F1: 0.6392
+education: Assoc-voc, Count: 283
+Precision: 0.8333 | Recall: 0.5233 | F1: 0.6429
+education: Bachelors, Count: 1,073
+Precision: 0.7332 | Recall: 0.7528 | F1: 0.7429
+education: Doctorate, Count: 85
+Precision: 0.7536 | Recall: 0.8966 | F1: 0.8189
+education: HS-grad, Count: 2,063
+Precision: 0.5864 | Recall: 0.3601 | F1: 0.4462
+education: Masters, Count: 329
+Precision: 0.8218 | Recall: 0.8034 | F1: 0.8125
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 120
+Precision: 0.9184 | Recall: 0.9677 | F1: 0.9424
+education: Some-college, Count: 1,458
+Precision: 0.6887 | Recall: 0.5069 | F1: 0.5840
+marital-status: Divorced, Count: 899
+Precision: 0.6512 | Recall: 0.3218 | F1: 0.4308
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 3,011
+Precision: 0.7336 | Recall: 0.6603 | F1: 0.6950
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+marital-status: Never-married, Count: 2,100
+Precision: 0.7800 | Recall: 0.4149 | F1: 0.5417
+marital-status: Separated, Count: 215
+Precision: 1.0000 | Recall: 0.4167 | F1: 0.5882
+marital-status: Widowed, Count: 207
+Precision: 1.0000 | Recall: 0.2222 | F1: 0.3636
+occupation: ?, Count: 359
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+occupation: Adm-clerical, Count: 755
+Precision: 0.6076 | Recall: 0.4615 | F1: 0.5246
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 849
+Precision: 0.6718 | Recall: 0.4378 | F1: 0.5301
+occupation: Exec-managerial, Count: 808
+Precision: 0.7836 | Recall: 0.7467 | F1: 0.7647
+occupation: Farming-fishing, Count: 201
+Precision: 0.7143 | Recall: 0.2273 | F1: 0.3448
+occupation: Handlers-cleaners, Count: 297
+Precision: 0.6667 | Recall: 0.2353 | F1: 0.3478
+occupation: Machine-op-inspct, Count: 408
+Precision: 0.6316 | Recall: 0.2400 | F1: 0.3478
+occupation: Other-service, Count: 626
+Precision: 0.7500 | Recall: 0.1200 | F1: 0.2069
+occupation: Priv-house-serv, Count: 32
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8000 | Recall: 0.7890 | F1: 0.7945
+occupation: Protective-serv, Count: 129
+Precision: 0.5714 | Recall: 0.5405 | F1: 0.5556
+occupation: Sales, Count: 729
+Precision: 0.6989 | Recall: 0.6468 | F1: 0.6718
+occupation: Tech-support, Count: 190
+Precision: 0.6721 | Recall: 0.6721 | F1: 0.6721
+occupation: Transport-moving, Count: 311
+Precision: 0.7059 | Recall: 0.4211 | F1: 0.5275
+relationship: Husband, Count: 2,694
+Precision: 0.7378 | Recall: 0.6656 | F1: 0.6998
+relationship: Not-in-family, Count: 1,648
+Precision: 0.7342 | Recall: 0.3742 | F1: 0.4957
+relationship: Other-relative, Count: 208
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+relationship: Own-child, Count: 969
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Unmarried, Count: 708
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Wife, Count: 285
+Precision: 0.7031 | Recall: 0.6294 | F1: 0.6642
+race: Amer-Indian-Eskimo, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 210
+Precision: 0.6383 | Recall: 0.5769 | F1: 0.6061
+race: Black, Count: 634
+Precision: 0.7581 | Recall: 0.5875 | F1: 0.6620
+race: Other, Count: 50
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+race: White, Count: 5,563
+Precision: 0.7378 | Recall: 0.6217 | F1: 0.6748
+sex: Female, Count: 2,124
+Precision: 0.7099 | Recall: 0.5251 | F1: 0.6037
+sex: Male, Count: 4,388
+Precision: 0.7381 | Recall: 0.6331 | F1: 0.6816
+native-country: ?, Count: 104
+Precision: 0.5714 | Recall: 0.6000 | F1: 0.5854
+native-country: Cambodia, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 25
+Precision: 0.7143 | Recall: 0.5556 | F1: 0.6250
+native-country: China, Count: 10
+Precision: 0.3333 | Recall: 1.0000 | F1: 0.5000
+native-country: Columbia, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 21
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Dominican-Republic, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 25
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: England, Count: 18
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: France, Count: 7
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Germany, Count: 27
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+native-country: Greece, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Holand-Netherlands, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 15
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: Iran, Count: 9
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Ireland, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 20
+Precision: 1.0000 | Recall: 0.5714 | F1: 0.7273
+native-country: Jamaica, Count: 14
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 16
+Precision: 0.5000 | Recall: 0.6667 | F1: 0.5714
+native-country: Laos, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Mexico, Count: 152
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+native-country: Nicaragua, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Outlying-US(Guam-USVI-etc), Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 37
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Poland, Count: 8
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Portugal, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 12
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Taiwan, Count: 14
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,818
+Precision: 0.7428 | Recall: 0.6205 | F1: 0.6762
+native-country: Vietnam, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 345
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+workclass: Federal-gov, Count: 200
+Precision: 0.7973 | Recall: 0.7375 | F1: 0.7662
+workclass: Local-gov, Count: 421
+Precision: 0.7434 | Recall: 0.6667 | F1: 0.7029
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,547
+Precision: 0.7450 | Recall: 0.5990 | F1: 0.6641
+workclass: Self-emp-inc, Count: 214
+Precision: 0.7769 | Recall: 0.8211 | F1: 0.7984
+workclass: Self-emp-not-inc, Count: 511
+Precision: 0.6667 | Recall: 0.5035 | F1: 0.5737
+workclass: State-gov, Count: 270
+Precision: 0.8364 | Recall: 0.6866 | F1: 0.7541
+workclass: Without-pay, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 10th, Count: 184
+Precision: 0.5000 | Recall: 0.1538 | F1: 0.2353
+education: 11th, Count: 240
+Precision: 1.0000 | Recall: 0.3636 | F1: 0.5333
+education: 12th, Count: 86
+Precision: 1.0000 | Recall: 0.1429 | F1: 0.2500
+education: 1st-4th, Count: 43
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+education: 5th-6th, Count: 67
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 7th-8th, Count: 104
+Precision: 0.6000 | Recall: 0.5000 | F1: 0.5455
+education: 9th, Count: 100
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: Assoc-acdm, Count: 205
+Precision: 0.7561 | Recall: 0.5849 | F1: 0.6596
+education: Assoc-voc, Count: 292
+Precision: 0.6667 | Recall: 0.5600 | F1: 0.6087
+education: Bachelors, Count: 1,068
+Precision: 0.7864 | Recall: 0.7522 | F1: 0.7689
+education: Doctorate, Count: 75
+Precision: 0.8548 | Recall: 0.9298 | F1: 0.8908
+education: HS-grad, Count: 2,105
+Precision: 0.6771 | Recall: 0.3746 | F1: 0.4824
+education: Masters, Count: 319
+Precision: 0.8034 | Recall: 0.8034 | F1: 0.8034
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 112
+Precision: 0.8471 | Recall: 0.9474 | F1: 0.8944
+education: Some-college, Count: 1,501
+Precision: 0.6481 | Recall: 0.5336 | F1: 0.5853
+marital-status: Divorced, Count: 869
+Precision: 0.7895 | Recall: 0.3448 | F1: 0.4800
+marital-status: Married-AF-spouse, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,996
+Precision: 0.7412 | Recall: 0.6679 | F1: 0.7026
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2222 | F1: 0.3333
+marital-status: Never-married, Count: 2,171
+Precision: 0.8718 | Recall: 0.3542 | F1: 0.5037
+marital-status: Separated, Count: 186
+Precision: 1.0000 | Recall: 0.3571 | F1: 0.5263
+marital-status: Widowed, Count: 187
+Precision: 1.0000 | Recall: 0.3077 | F1: 0.4706
+occupation: ?, Count: 346
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+occupation: Adm-clerical, Count: 791
+Precision: 0.6495 | Recall: 0.5431 | F1: 0.5915
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 820
+Precision: 0.5909 | Recall: 0.4358 | F1: 0.5016
+occupation: Exec-managerial, Count: 791
+Precision: 0.8072 | Recall: 0.7792 | F1: 0.7929
+occupation: Farming-fishing, Count: 203
+Precision: 0.7857 | Recall: 0.4783 | F1: 0.5946
+occupation: Handlers-cleaners, Count: 288
+Precision: 0.6667 | Recall: 0.1739 | F1: 0.2759
+occupation: Machine-op-inspct, Count: 427
+Precision: 0.6552 | Recall: 0.3725 | F1: 0.4750
+occupation: Other-service, Count: 647
+Precision: 0.8333 | Recall: 0.1562 | F1: 0.2632
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8128 | Recall: 0.7951 | F1: 0.8039
+occupation: Protective-serv, Count: 130
+Precision: 0.7353 | Recall: 0.5814 | F1: 0.6494
+occupation: Sales, Count: 739
+Precision: 0.6918 | Recall: 0.5288 | F1: 0.5994
+occupation: Tech-support, Count: 180
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+occupation: Transport-moving, Count: 306
+Precision: 0.6000 | Recall: 0.2679 | F1: 0.3704
+relationship: Husband, Count: 2,631
+Precision: 0.7392 | Recall: 0.6661 | F1: 0.7008
+relationship: Not-in-family, Count: 1,650
+Precision: 0.8451 | Recall: 0.3488 | F1: 0.4938
+relationship: Other-relative, Count: 184
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,082
+Precision: 1.0000 | Recall: 0.2941 | F1: 0.4545
+relationship: Unmarried, Count: 656
+Precision: 0.7857 | Recall: 0.3056 | F1: 0.4400
+relationship: Wife, Count: 309
+Precision: 0.7434 | Recall: 0.7107 | F1: 0.7267
+race: Amer-Indian-Eskimo, Count: 65
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+race: Asian-Pac-Islander, Count: 207
+Precision: 0.7273 | Recall: 0.5714 | F1: 0.6400
+race: Black, Count: 621
+Precision: 0.8333 | Recall: 0.5495 | F1: 0.6623
+race: Other, Count: 67
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+race: White, Count: 5,552
+Precision: 0.7450 | Recall: 0.6301 | F1: 0.6828
+sex: Female, Count: 2,130
+Precision: 0.7622 | Recall: 0.5851 | F1: 0.6620
+sex: Male, Count: 4,382
+Precision: 0.7451 | Recall: 0.6289 | F1: 0.6821
+native-country: ?, Count: 127
+Precision: 0.8065 | Recall: 0.6579 | F1: 0.7246
+native-country: Cambodia, Count: 2
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Canada, Count: 22
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+native-country: Columbia, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 16
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Dominican-Republic, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: El-Salvador, Count: 24
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 19
+Precision: 0.8750 | Recall: 0.7000 | F1: 0.7778
+native-country: France, Count: 5
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Germany, Count: 28
+Precision: 1.0000 | Recall: 0.5556 | F1: 0.7143
+native-country: Greece, Count: 7
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.7000 | Recall: 1.0000 | F1: 0.8235
+native-country: Iran, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Italy, Count: 14
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+native-country: Jamaica, Count: 19
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.7143 | F1: 0.8333
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 132
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+native-country: Nicaragua, Count: 9
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 38
+Precision: 0.5556 | Recall: 0.5000 | F1: 0.5263
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Puerto-Rico, Count: 19
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: South, Count: 18
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Taiwan, Count: 8
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: United-States, Count: 5,820
+Precision: 0.7438 | Recall: 0.6265 | F1: 0.6801
+native-country: Vietnam, Count: 14
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 377
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+workclass: Federal-gov, Count: 188
+Precision: 0.8136 | Recall: 0.6486 | F1: 0.7218
+workclass: Local-gov, Count: 415
+Precision: 0.7451 | Recall: 0.6129 | F1: 0.6726
+workclass: Never-worked, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,492
+Precision: 0.7312 | Recall: 0.6145 | F1: 0.6678
+workclass: Self-emp-inc, Count: 234
+Precision: 0.8222 | Recall: 0.8538 | F1: 0.8377
+workclass: Self-emp-not-inc, Count: 538
+Precision: 0.7607 | Recall: 0.5973 | F1: 0.6692
+workclass: State-gov, Count: 263
+Precision: 0.7705 | Recall: 0.6620 | F1: 0.7121
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 189
+Precision: 1.0000 | Recall: 0.0769 | F1: 0.1429
+education: 11th, Count: 233
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+education: 12th, Count: 87
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 1st-4th, Count: 31
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 123
+Precision: 0.5000 | Recall: 0.1429 | F1: 0.2222
+education: 9th, Count: 105
+Precision: 1.0000 | Recall: 0.1250 | F1: 0.2222
+education: Assoc-acdm, Count: 240
+Precision: 0.6275 | Recall: 0.5424 | F1: 0.5818
+education: Assoc-voc, Count: 283
+Precision: 0.6774 | Recall: 0.5600 | F1: 0.6131
+education: Bachelors, Count: 1,090
+Precision: 0.7551 | Recall: 0.7620 | F1: 0.7585
+education: Doctorate, Count: 93
+Precision: 0.8873 | Recall: 0.8514 | F1: 0.8690
+education: HS-grad, Count: 2,091
+Precision: 0.6590 | Recall: 0.4145 | F1: 0.5089
+education: Masters, Count: 355
+Precision: 0.8564 | Recall: 0.8350 | F1: 0.8456
+education: Preschool, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 111
+Precision: 0.8571 | Recall: 0.8780 | F1: 0.8675
+education: Some-college, Count: 1,413
+Precision: 0.6582 | Recall: 0.5265 | F1: 0.5850
+marital-status: Divorced, Count: 902
+Precision: 0.7857 | Recall: 0.3333 | F1: 0.4681
+marital-status: Married-AF-spouse, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,975
+Precision: 0.7359 | Recall: 0.6762 | F1: 0.7048
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+marital-status: Never-married, Count: 2,162
+Precision: 0.9020 | Recall: 0.4742 | F1: 0.6216
+marital-status: Separated, Count: 191
+Precision: 1.0000 | Recall: 0.2308 | F1: 0.3750
+marital-status: Widowed, Count: 200
+Precision: 0.8333 | Recall: 0.2778 | F1: 0.4167
+occupation: ?, Count: 380
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+occupation: Adm-clerical, Count: 730
+Precision: 0.6538 | Recall: 0.5543 | F1: 0.6000
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 816
+Precision: 0.6165 | Recall: 0.4339 | F1: 0.5093
+occupation: Exec-managerial, Count: 823
+Precision: 0.7805 | Recall: 0.8060 | F1: 0.7931
+occupation: Farming-fishing, Count: 201
+Precision: 0.8000 | Recall: 0.4615 | F1: 0.5854
+occupation: Handlers-cleaners, Count: 241
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+occupation: Machine-op-inspct, Count: 397
+Precision: 0.5294 | Recall: 0.3396 | F1: 0.4138
+occupation: Other-service, Count: 696
+Precision: 0.8571 | Recall: 0.2143 | F1: 0.3429
+occupation: Priv-house-serv, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 823
+Precision: 0.7943 | Recall: 0.7943 | F1: 0.7943
+occupation: Protective-serv, Count: 106
+Precision: 0.8276 | Recall: 0.5455 | F1: 0.6575
+occupation: Sales, Count: 767
+Precision: 0.7576 | Recall: 0.5787 | F1: 0.6562
+occupation: Tech-support, Count: 191
+Precision: 0.8140 | Recall: 0.5224 | F1: 0.6364
+occupation: Transport-moving, Count: 310
+Precision: 0.6341 | Recall: 0.3881 | F1: 0.4815
+relationship: Husband, Count: 2,626
+Precision: 0.7342 | Recall: 0.6802 | F1: 0.7062
+relationship: Not-in-family, Count: 1,666
+Precision: 0.8353 | Recall: 0.4152 | F1: 0.5547
+relationship: Other-relative, Count: 212
+Precision: 1.0000 | Recall: 0.1000 | F1: 0.1818
+relationship: Own-child, Count: 998
+Precision: 1.0000 | Recall: 0.4444 | F1: 0.6154
+relationship: Unmarried, Count: 707
+Precision: 0.9444 | Recall: 0.3269 | F1: 0.4857
+relationship: Wife, Count: 303
+Precision: 0.7419 | Recall: 0.6389 | F1: 0.6866
+race: Amer-Indian-Eskimo, Count: 53
+Precision: 0.3333 | Recall: 0.1667 | F1: 0.2222
+race: Asian-Pac-Islander, Count: 224
+Precision: 0.7333 | Recall: 0.6600 | F1: 0.6947
+race: Black, Count: 670
+Precision: 0.7895 | Recall: 0.5625 | F1: 0.6569
+race: Other, Count: 47
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+race: White, Count: 5,518
+Precision: 0.7453 | Recall: 0.6352 | F1: 0.6858
+sex: Female, Count: 2,196
+Precision: 0.7738 | Recall: 0.5221 | F1: 0.6235
+sex: Male, Count: 4,316
+Precision: 0.7412 | Recall: 0.6513 | F1: 0.6933
+native-country: ?, Count: 121
+Precision: 0.7692 | Recall: 0.7407 | F1: 0.7547
+native-country: Cambodia, Count: 4
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Canada, Count: 26
+Precision: 0.8571 | Recall: 0.8571 | F1: 0.8571
+native-country: China, Count: 17
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Columbia, Count: 17
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Cuba, Count: 12
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Dominican-Republic, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: England, Count: 19
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: France, Count: 6
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Germany, Count: 30
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Greece, Count: 10
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Guatemala, Count: 15
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.6667 | Recall: 0.7500 | F1: 0.7059
+native-country: Iran, Count: 11
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Ireland, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.3333 | Recall: 0.2500 | F1: 0.2857
+native-country: Jamaica, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Laos, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 125
+Precision: 0.8333 | Recall: 0.5556 | F1: 0.6667
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 47
+Precision: 0.8889 | Recall: 0.6154 | F1: 0.7273
+native-country: Poland, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 20
+Precision: 0.2500 | Recall: 0.3333 | F1: 0.2857
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,806
+Precision: 0.7448 | Recall: 0.6257 | F1: 0.6801
+native-country: Vietnam, Count: 15
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+workclass: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+workclass: Federal-gov, Count: 191
+Precision: 0.7571 | Recall: 0.7571 | F1: 0.7571
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6545 | F1: 0.6990
+workclass: Private, Count: 4,578
+Precision: 0.7447 | Recall: 0.6324 | F1: 0.6840
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7565 | Recall: 0.7373 | F1: 0.7468
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7339 | Recall: 0.5096 | F1: 0.6015
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6575 | F1: 0.7007
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.4286 | Recall: 0.2500 | F1: 0.3158
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.6923 | Recall: 0.5745 | F1: 0.6279
+education: Assoc-voc, Count: 273
+Precision: 0.6667 | Recall: 0.5397 | F1: 0.5965
+education: Bachelors, Count: 1,053
+Precision: 0.7564 | Recall: 0.7244 | F1: 0.7401
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6622 | Recall: 0.4261 | F1: 0.5185
+education: Masters, Count: 369
+Precision: 0.8309 | Recall: 0.8309 | F1: 0.8309
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8191 | Recall: 0.9167 | F1: 0.8652
+education: Some-college, Count: 1,485
+Precision: 0.6952 | Recall: 0.5271 | F1: 0.5996
+marital-status: Divorced, Count: 920
+Precision: 0.7826 | Recall: 0.3495 | F1: 0.4832
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7377 | Recall: 0.6816 | F1: 0.7085
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8182 | Recall: 0.4369 | F1: 0.5696
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+occupation: Adm-clerical, Count: 726
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6641 | Recall: 0.4696 | F1: 0.5502
+occupation: Exec-managerial, Count: 838
+Precision: 0.8011 | Recall: 0.7506 | F1: 0.7750
+occupation: Farming-fishing, Count: 193
+Precision: 0.6364 | Recall: 0.2500 | F1: 0.3590
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5938 | Recall: 0.4043 | F1: 0.4810
+occupation: Other-service, Count: 667
+Precision: 0.8571 | Recall: 0.2308 | F1: 0.3636
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7882 | Recall: 0.7500 | F1: 0.7686
+occupation: Protective-serv, Count: 136
+Precision: 0.6970 | Recall: 0.5476 | F1: 0.6133
+occupation: Sales, Count: 729
+Precision: 0.7333 | Recall: 0.6875 | F1: 0.7097
+occupation: Tech-support, Count: 189
+Precision: 0.6744 | Recall: 0.5686 | F1: 0.6170
+occupation: Transport-moving, Count: 317
+Precision: 0.6000 | Recall: 0.4219 | F1: 0.4954
+relationship: Husband, Count: 2,590
+Precision: 0.7401 | Recall: 0.6838 | F1: 0.7108
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8021 | Recall: 0.4096 | F1: 0.5423
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9231 | Recall: 0.2667 | F1: 0.4138
+relationship: Wife, Count: 322
+Precision: 0.7164 | Recall: 0.6713 | F1: 0.6931
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6250 | Recall: 0.5000 | F1: 0.5556
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7547 | Recall: 0.6452 | F1: 0.6957
+race: Black, Count: 599
+Precision: 0.7692 | Recall: 0.6154 | F1: 0.6838
+race: Other, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: White, Count: 5,595
+Precision: 0.7448 | Recall: 0.6317 | F1: 0.6836
+sex: Female, Count: 2,126
+Precision: 0.7267 | Recall: 0.5021 | F1: 0.5939
+sex: Male, Count: 4,387
+Precision: 0.7476 | Recall: 0.6532 | F1: 0.6972
+native-country: ?, Count: 125
+Precision: 0.7097 | Recall: 0.7097 | F1: 0.7097
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 22
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 0.8750 | F1: 0.9333
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8333 | Recall: 0.7692 | F1: 0.8000
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.7778 | Recall: 0.8750 | F1: 0.8235
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6250 | F1: 0.7692
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7448 | Recall: 0.6265 | F1: 0.6805
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 368
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+workclass: Federal-gov, Count: 186
+Precision: 0.7424 | Recall: 0.6447 | F1: 0.6901
+workclass: Local-gov, Count: 475
+Precision: 0.6912 | Recall: 0.6714 | F1: 0.6812
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,513
+Precision: 0.6974 | Recall: 0.6033 | F1: 0.6469
+workclass: Self-emp-inc, Count: 231
+Precision: 0.8319 | Recall: 0.7557 | F1: 0.7920
+workclass: Self-emp-not-inc, Count: 480
+Precision: 0.6598 | Recall: 0.5039 | F1: 0.5714
+workclass: State-gov, Count: 256
+Precision: 0.7188 | Recall: 0.7302 | F1: 0.7244
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 197
+Precision: 0.5000 | Recall: 0.0833 | F1: 0.1429
+education: 11th, Count: 235
+Precision: 0.8000 | Recall: 0.2857 | F1: 0.4211
+education: 12th, Count: 77
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 1st-4th, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 70
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 118
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 9th, Count: 84
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 225
+Precision: 0.6078 | Recall: 0.5741 | F1: 0.5905
+education: Assoc-voc, Count: 251
+Precision: 0.6341 | Recall: 0.4194 | F1: 0.5049
+education: Bachelors, Count: 1,071
+Precision: 0.7460 | Recall: 0.7600 | F1: 0.7529
+education: Doctorate, Count: 83
+Precision: 0.7812 | Recall: 0.8333 | F1: 0.8065
+education: HS-grad, Count: 2,157
+Precision: 0.5520 | Recall: 0.4220 | F1: 0.4783
+education: Masters, Count: 351
+Precision: 0.7949 | Recall: 0.7908 | F1: 0.7928
+education: Preschool, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: Prof-school, Count: 117
+Precision: 0.8696 | Recall: 0.9091 | F1: 0.8889
+education: Some-college, Count: 1,434
+Precision: 0.6761 | Recall: 0.4898 | F1: 0.5680
+marital-status: Divorced, Count: 853
+Precision: 0.8108 | Recall: 0.3448 | F1: 0.4839
+marital-status: Married-AF-spouse, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+marital-status: Married-civ-spouse, Count: 3,044
+Precision: 0.6967 | Recall: 0.6535 | F1: 0.6744
+marital-status: Married-spouse-absent, Count: 74
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+marital-status: Never-married, Count: 2,124
+Precision: 0.9070 | Recall: 0.3861 | F1: 0.5417
+marital-status: Separated, Count: 224
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+marital-status: Widowed, Count: 191
+Precision: 0.8889 | Recall: 0.4706 | F1: 0.6154
+occupation: ?, Count: 369
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+occupation: Adm-clerical, Count: 768
+Precision: 0.6364 | Recall: 0.4949 | F1: 0.5568
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 793
+Precision: 0.6000 | Recall: 0.4693 | F1: 0.5266
+occupation: Exec-managerial, Count: 806
+Precision: 0.7886 | Recall: 0.7500 | F1: 0.7688
+occupation: Farming-fishing, Count: 196
+Precision: 0.6000 | Recall: 0.3750 | F1: 0.4615
+occupation: Handlers-cleaners, Count: 271
+Precision: 0.6667 | Recall: 0.1053 | F1: 0.1818
+occupation: Machine-op-inspct, Count: 392
+Precision: 0.5789 | Recall: 0.2245 | F1: 0.3235
+occupation: Other-service, Count: 659
+Precision: 0.6667 | Recall: 0.1538 | F1: 0.2500
+occupation: Priv-house-serv, Count: 36
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 855
+Precision: 0.7454 | Recall: 0.7358 | F1: 0.7405
+occupation: Protective-serv, Count: 148
+Precision: 0.6170 | Recall: 0.6444 | F1: 0.6304
+occupation: Sales, Count: 686
+Precision: 0.6570 | Recall: 0.6175 | F1: 0.6366
+occupation: Tech-support, Count: 178
+Precision: 0.7037 | Recall: 0.7917 | F1: 0.7451
+occupation: Transport-moving, Count: 353
+Precision: 0.6122 | Recall: 0.3947 | F1: 0.4800
+relationship: Husband, Count: 2,652
+Precision: 0.7039 | Recall: 0.6520 | F1: 0.6770
+relationship: Not-in-family, Count: 1,639
+Precision: 0.8659 | Recall: 0.4176 | F1: 0.5635
+relationship: Other-relative, Count: 199
+Precision: 0.5000 | Recall: 0.1667 | F1: 0.2500
+relationship: Own-child, Count: 1,000
+Precision: 0.7500 | Recall: 0.2143 | F1: 0.3333
+relationship: Unmarried, Count: 673
+Precision: 0.7857 | Recall: 0.3143 | F1: 0.4490
+relationship: Wife, Count: 349
+Precision: 0.6564 | Recall: 0.6859 | F1: 0.6708
+race: Amer-Indian-Eskimo, Count: 67
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 205
+Precision: 0.7561 | Recall: 0.5536 | F1: 0.6392
+race: Black, Count: 600
+Precision: 0.6078 | Recall: 0.4366 | F1: 0.5082
+race: Other, Count: 52
+Precision: 0.6667 | Recall: 0.4000 | F1: 0.5000
+race: White, Count: 5,588
+Precision: 0.7102 | Recall: 0.6295 | F1: 0.6674
+sex: Female, Count: 2,195
+Precision: 0.6869 | Recall: 0.5738 | F1: 0.6253
+sex: Male, Count: 4,317
+Precision: 0.7124 | Recall: 0.6241 | F1: 0.6653
+native-country: ?, Count: 106
+Precision: 0.8182 | Recall: 0.6000 | F1: 0.6923
+native-country: Cambodia, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 26
+Precision: 0.7500 | Recall: 0.3750 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+native-country: Columbia, Count: 12
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 27
+Precision: 0.8750 | Recall: 0.7778 | F1: 0.8235
+native-country: Dominican-Republic, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: El-Salvador, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 20
+Precision: 0.7500 | Recall: 0.4286 | F1: 0.5455
+native-country: France, Count: 6
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Germany, Count: 20
+Precision: 0.6667 | Recall: 0.2857 | F1: 0.4000
+native-country: Greece, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Guatemala, Count: 10
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 12
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 2
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: India, Count: 24
+Precision: 0.8571 | Recall: 0.6667 | F1: 0.7500
+native-country: Iran, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Ireland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 11
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Jamaica, Count: 21
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 9
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 120
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Nicaragua, Count: 6
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 41
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+native-country: Poland, Count: 13
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Puerto-Rico, Count: 19
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Scotland, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: South, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: United-States, Count: 5,856
+Precision: 0.7068 | Recall: 0.6225 | F1: 0.6619
+native-country: Vietnam, Count: 13
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+workclass: ?, Count: 357
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+workclass: Federal-gov, Count: 195
+Precision: 0.7179 | Recall: 0.7887 | F1: 0.7517
+workclass: Local-gov, Count: 395
+Precision: 0.6939 | Recall: 0.5812 | F1: 0.6326
+workclass: Never-worked, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,566
+Precision: 0.7378 | Recall: 0.6123 | F1: 0.6692
+workclass: Self-emp-inc, Count: 225
+Precision: 0.7826 | Recall: 0.7500 | F1: 0.7660
+workclass: Self-emp-not-inc, Count: 514
+Precision: 0.7475 | Recall: 0.5000 | F1: 0.5992
+workclass: State-gov, Count: 255
+Precision: 0.7273 | Recall: 0.7089 | F1: 0.7179
+workclass: Without-pay, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 180
+Precision: 0.7500 | Recall: 0.5000 | F1: 0.6000
+education: 11th, Count: 242
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: 12th, Count: 85
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 1st-4th, Count: 42
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 72
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 160
+Precision: 0.5000 | Recall: 0.1000 | F1: 0.1667
+education: 9th, Count: 110
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 199
+Precision: 0.6889 | Recall: 0.5962 | F1: 0.6392
+education: Assoc-voc, Count: 283
+Precision: 0.8333 | Recall: 0.5233 | F1: 0.6429
+education: Bachelors, Count: 1,073
+Precision: 0.7332 | Recall: 0.7528 | F1: 0.7429
+education: Doctorate, Count: 85
+Precision: 0.7536 | Recall: 0.8966 | F1: 0.8189
+education: HS-grad, Count: 2,063
+Precision: 0.5864 | Recall: 0.3601 | F1: 0.4462
+education: Masters, Count: 329
+Precision: 0.8218 | Recall: 0.8034 | F1: 0.8125
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 120
+Precision: 0.9184 | Recall: 0.9677 | F1: 0.9424
+education: Some-college, Count: 1,458
+Precision: 0.6887 | Recall: 0.5069 | F1: 0.5840
+marital-status: Divorced, Count: 899
+Precision: 0.6512 | Recall: 0.3218 | F1: 0.4308
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 3,011
+Precision: 0.7336 | Recall: 0.6603 | F1: 0.6950
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+marital-status: Never-married, Count: 2,100
+Precision: 0.7800 | Recall: 0.4149 | F1: 0.5417
+marital-status: Separated, Count: 215
+Precision: 1.0000 | Recall: 0.4167 | F1: 0.5882
+marital-status: Widowed, Count: 207
+Precision: 1.0000 | Recall: 0.2222 | F1: 0.3636
+occupation: ?, Count: 359
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+occupation: Adm-clerical, Count: 755
+Precision: 0.6076 | Recall: 0.4615 | F1: 0.5246
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 849
+Precision: 0.6718 | Recall: 0.4378 | F1: 0.5301
+occupation: Exec-managerial, Count: 808
+Precision: 0.7836 | Recall: 0.7467 | F1: 0.7647
+occupation: Farming-fishing, Count: 201
+Precision: 0.7143 | Recall: 0.2273 | F1: 0.3448
+occupation: Handlers-cleaners, Count: 297
+Precision: 0.6667 | Recall: 0.2353 | F1: 0.3478
+occupation: Machine-op-inspct, Count: 408
+Precision: 0.6316 | Recall: 0.2400 | F1: 0.3478
+occupation: Other-service, Count: 626
+Precision: 0.7500 | Recall: 0.1200 | F1: 0.2069
+occupation: Priv-house-serv, Count: 32
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8000 | Recall: 0.7890 | F1: 0.7945
+occupation: Protective-serv, Count: 129
+Precision: 0.5714 | Recall: 0.5405 | F1: 0.5556
+occupation: Sales, Count: 729
+Precision: 0.6989 | Recall: 0.6468 | F1: 0.6718
+occupation: Tech-support, Count: 190
+Precision: 0.6721 | Recall: 0.6721 | F1: 0.6721
+occupation: Transport-moving, Count: 311
+Precision: 0.7059 | Recall: 0.4211 | F1: 0.5275
+relationship: Husband, Count: 2,694
+Precision: 0.7378 | Recall: 0.6656 | F1: 0.6998
+relationship: Not-in-family, Count: 1,648
+Precision: 0.7342 | Recall: 0.3742 | F1: 0.4957
+relationship: Other-relative, Count: 208
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+relationship: Own-child, Count: 969
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Unmarried, Count: 708
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Wife, Count: 285
+Precision: 0.7031 | Recall: 0.6294 | F1: 0.6642
+race: Amer-Indian-Eskimo, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 210
+Precision: 0.6383 | Recall: 0.5769 | F1: 0.6061
+race: Black, Count: 634
+Precision: 0.7581 | Recall: 0.5875 | F1: 0.6620
+race: Other, Count: 50
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+race: White, Count: 5,563
+Precision: 0.7378 | Recall: 0.6217 | F1: 0.6748
+sex: Female, Count: 2,124
+Precision: 0.7099 | Recall: 0.5251 | F1: 0.6037
+sex: Male, Count: 4,388
+Precision: 0.7381 | Recall: 0.6331 | F1: 0.6816
+native-country: ?, Count: 104
+Precision: 0.5714 | Recall: 0.6000 | F1: 0.5854
+native-country: Cambodia, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 25
+Precision: 0.7143 | Recall: 0.5556 | F1: 0.6250
+native-country: China, Count: 10
+Precision: 0.3333 | Recall: 1.0000 | F1: 0.5000
+native-country: Columbia, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 21
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Dominican-Republic, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 25
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: England, Count: 18
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: France, Count: 7
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Germany, Count: 27
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+native-country: Greece, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Holand-Netherlands, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 15
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: Iran, Count: 9
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Ireland, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 20
+Precision: 1.0000 | Recall: 0.5714 | F1: 0.7273
+native-country: Jamaica, Count: 14
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 16
+Precision: 0.5000 | Recall: 0.6667 | F1: 0.5714
+native-country: Laos, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Mexico, Count: 152
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+native-country: Nicaragua, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Outlying-US(Guam-USVI-etc), Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 37
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Poland, Count: 8
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Portugal, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 12
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Taiwan, Count: 14
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,818
+Precision: 0.7428 | Recall: 0.6205 | F1: 0.6762
+native-country: Vietnam, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 345
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+workclass: Federal-gov, Count: 200
+Precision: 0.7973 | Recall: 0.7375 | F1: 0.7662
+workclass: Local-gov, Count: 421
+Precision: 0.7434 | Recall: 0.6667 | F1: 0.7029
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,547
+Precision: 0.7450 | Recall: 0.5990 | F1: 0.6641
+workclass: Self-emp-inc, Count: 214
+Precision: 0.7769 | Recall: 0.8211 | F1: 0.7984
+workclass: Self-emp-not-inc, Count: 511
+Precision: 0.6667 | Recall: 0.5035 | F1: 0.5737
+workclass: State-gov, Count: 270
+Precision: 0.8364 | Recall: 0.6866 | F1: 0.7541
+workclass: Without-pay, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 10th, Count: 184
+Precision: 0.5000 | Recall: 0.1538 | F1: 0.2353
+education: 11th, Count: 240
+Precision: 1.0000 | Recall: 0.3636 | F1: 0.5333
+education: 12th, Count: 86
+Precision: 1.0000 | Recall: 0.1429 | F1: 0.2500
+education: 1st-4th, Count: 43
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+education: 5th-6th, Count: 67
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 7th-8th, Count: 104
+Precision: 0.6000 | Recall: 0.5000 | F1: 0.5455
+education: 9th, Count: 100
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: Assoc-acdm, Count: 205
+Precision: 0.7561 | Recall: 0.5849 | F1: 0.6596
+education: Assoc-voc, Count: 292
+Precision: 0.6667 | Recall: 0.5600 | F1: 0.6087
+education: Bachelors, Count: 1,068
+Precision: 0.7864 | Recall: 0.7522 | F1: 0.7689
+education: Doctorate, Count: 75
+Precision: 0.8548 | Recall: 0.9298 | F1: 0.8908
+education: HS-grad, Count: 2,105
+Precision: 0.6771 | Recall: 0.3746 | F1: 0.4824
+education: Masters, Count: 319
+Precision: 0.8034 | Recall: 0.8034 | F1: 0.8034
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 112
+Precision: 0.8471 | Recall: 0.9474 | F1: 0.8944
+education: Some-college, Count: 1,501
+Precision: 0.6481 | Recall: 0.5336 | F1: 0.5853
+marital-status: Divorced, Count: 869
+Precision: 0.7895 | Recall: 0.3448 | F1: 0.4800
+marital-status: Married-AF-spouse, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,996
+Precision: 0.7412 | Recall: 0.6679 | F1: 0.7026
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2222 | F1: 0.3333
+marital-status: Never-married, Count: 2,171
+Precision: 0.8718 | Recall: 0.3542 | F1: 0.5037
+marital-status: Separated, Count: 186
+Precision: 1.0000 | Recall: 0.3571 | F1: 0.5263
+marital-status: Widowed, Count: 187
+Precision: 1.0000 | Recall: 0.3077 | F1: 0.4706
+occupation: ?, Count: 346
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+occupation: Adm-clerical, Count: 791
+Precision: 0.6495 | Recall: 0.5431 | F1: 0.5915
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 820
+Precision: 0.5909 | Recall: 0.4358 | F1: 0.5016
+occupation: Exec-managerial, Count: 791
+Precision: 0.8072 | Recall: 0.7792 | F1: 0.7929
+occupation: Farming-fishing, Count: 203
+Precision: 0.7857 | Recall: 0.4783 | F1: 0.5946
+occupation: Handlers-cleaners, Count: 288
+Precision: 0.6667 | Recall: 0.1739 | F1: 0.2759
+occupation: Machine-op-inspct, Count: 427
+Precision: 0.6552 | Recall: 0.3725 | F1: 0.4750
+occupation: Other-service, Count: 647
+Precision: 0.8333 | Recall: 0.1562 | F1: 0.2632
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8128 | Recall: 0.7951 | F1: 0.8039
+occupation: Protective-serv, Count: 130
+Precision: 0.7353 | Recall: 0.5814 | F1: 0.6494
+occupation: Sales, Count: 739
+Precision: 0.6918 | Recall: 0.5288 | F1: 0.5994
+occupation: Tech-support, Count: 180
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+occupation: Transport-moving, Count: 306
+Precision: 0.6000 | Recall: 0.2679 | F1: 0.3704
+relationship: Husband, Count: 2,631
+Precision: 0.7392 | Recall: 0.6661 | F1: 0.7008
+relationship: Not-in-family, Count: 1,650
+Precision: 0.8451 | Recall: 0.3488 | F1: 0.4938
+relationship: Other-relative, Count: 184
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,082
+Precision: 1.0000 | Recall: 0.2941 | F1: 0.4545
+relationship: Unmarried, Count: 656
+Precision: 0.7857 | Recall: 0.3056 | F1: 0.4400
+relationship: Wife, Count: 309
+Precision: 0.7434 | Recall: 0.7107 | F1: 0.7267
+race: Amer-Indian-Eskimo, Count: 65
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+race: Asian-Pac-Islander, Count: 207
+Precision: 0.7273 | Recall: 0.5714 | F1: 0.6400
+race: Black, Count: 621
+Precision: 0.8333 | Recall: 0.5495 | F1: 0.6623
+race: Other, Count: 67
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+race: White, Count: 5,552
+Precision: 0.7450 | Recall: 0.6301 | F1: 0.6828
+sex: Female, Count: 2,130
+Precision: 0.7622 | Recall: 0.5851 | F1: 0.6620
+sex: Male, Count: 4,382
+Precision: 0.7451 | Recall: 0.6289 | F1: 0.6821
+native-country: ?, Count: 127
+Precision: 0.8065 | Recall: 0.6579 | F1: 0.7246
+native-country: Cambodia, Count: 2
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Canada, Count: 22
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+native-country: Columbia, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 16
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Dominican-Republic, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: El-Salvador, Count: 24
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 19
+Precision: 0.8750 | Recall: 0.7000 | F1: 0.7778
+native-country: France, Count: 5
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Germany, Count: 28
+Precision: 1.0000 | Recall: 0.5556 | F1: 0.7143
+native-country: Greece, Count: 7
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.7000 | Recall: 1.0000 | F1: 0.8235
+native-country: Iran, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Italy, Count: 14
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+native-country: Jamaica, Count: 19
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.7143 | F1: 0.8333
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 132
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+native-country: Nicaragua, Count: 9
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 38
+Precision: 0.5556 | Recall: 0.5000 | F1: 0.5263
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Puerto-Rico, Count: 19
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: South, Count: 18
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Taiwan, Count: 8
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: United-States, Count: 5,820
+Precision: 0.7438 | Recall: 0.6265 | F1: 0.6801
+native-country: Vietnam, Count: 14
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 377
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+workclass: Federal-gov, Count: 188
+Precision: 0.8136 | Recall: 0.6486 | F1: 0.7218
+workclass: Local-gov, Count: 415
+Precision: 0.7451 | Recall: 0.6129 | F1: 0.6726
+workclass: Never-worked, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,492
+Precision: 0.7312 | Recall: 0.6145 | F1: 0.6678
+workclass: Self-emp-inc, Count: 234
+Precision: 0.8222 | Recall: 0.8538 | F1: 0.8377
+workclass: Self-emp-not-inc, Count: 538
+Precision: 0.7607 | Recall: 0.5973 | F1: 0.6692
+workclass: State-gov, Count: 263
+Precision: 0.7705 | Recall: 0.6620 | F1: 0.7121
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 189
+Precision: 1.0000 | Recall: 0.0769 | F1: 0.1429
+education: 11th, Count: 233
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+education: 12th, Count: 87
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 1st-4th, Count: 31
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 123
+Precision: 0.5000 | Recall: 0.1429 | F1: 0.2222
+education: 9th, Count: 105
+Precision: 1.0000 | Recall: 0.1250 | F1: 0.2222
+education: Assoc-acdm, Count: 240
+Precision: 0.6275 | Recall: 0.5424 | F1: 0.5818
+education: Assoc-voc, Count: 283
+Precision: 0.6774 | Recall: 0.5600 | F1: 0.6131
+education: Bachelors, Count: 1,090
+Precision: 0.7551 | Recall: 0.7620 | F1: 0.7585
+education: Doctorate, Count: 93
+Precision: 0.8873 | Recall: 0.8514 | F1: 0.8690
+education: HS-grad, Count: 2,091
+Precision: 0.6590 | Recall: 0.4145 | F1: 0.5089
+education: Masters, Count: 355
+Precision: 0.8564 | Recall: 0.8350 | F1: 0.8456
+education: Preschool, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 111
+Precision: 0.8571 | Recall: 0.8780 | F1: 0.8675
+education: Some-college, Count: 1,413
+Precision: 0.6582 | Recall: 0.5265 | F1: 0.5850
+marital-status: Divorced, Count: 902
+Precision: 0.7857 | Recall: 0.3333 | F1: 0.4681
+marital-status: Married-AF-spouse, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,975
+Precision: 0.7359 | Recall: 0.6762 | F1: 0.7048
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+marital-status: Never-married, Count: 2,162
+Precision: 0.9020 | Recall: 0.4742 | F1: 0.6216
+marital-status: Separated, Count: 191
+Precision: 1.0000 | Recall: 0.2308 | F1: 0.3750
+marital-status: Widowed, Count: 200
+Precision: 0.8333 | Recall: 0.2778 | F1: 0.4167
+occupation: ?, Count: 380
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+occupation: Adm-clerical, Count: 730
+Precision: 0.6538 | Recall: 0.5543 | F1: 0.6000
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 816
+Precision: 0.6165 | Recall: 0.4339 | F1: 0.5093
+occupation: Exec-managerial, Count: 823
+Precision: 0.7805 | Recall: 0.8060 | F1: 0.7931
+occupation: Farming-fishing, Count: 201
+Precision: 0.8000 | Recall: 0.4615 | F1: 0.5854
+occupation: Handlers-cleaners, Count: 241
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+occupation: Machine-op-inspct, Count: 397
+Precision: 0.5294 | Recall: 0.3396 | F1: 0.4138
+occupation: Other-service, Count: 696
+Precision: 0.8571 | Recall: 0.2143 | F1: 0.3429
+occupation: Priv-house-serv, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 823
+Precision: 0.7943 | Recall: 0.7943 | F1: 0.7943
+occupation: Protective-serv, Count: 106
+Precision: 0.8276 | Recall: 0.5455 | F1: 0.6575
+occupation: Sales, Count: 767
+Precision: 0.7576 | Recall: 0.5787 | F1: 0.6562
+occupation: Tech-support, Count: 191
+Precision: 0.8140 | Recall: 0.5224 | F1: 0.6364
+occupation: Transport-moving, Count: 310
+Precision: 0.6341 | Recall: 0.3881 | F1: 0.4815
+relationship: Husband, Count: 2,626
+Precision: 0.7342 | Recall: 0.6802 | F1: 0.7062
+relationship: Not-in-family, Count: 1,666
+Precision: 0.8353 | Recall: 0.4152 | F1: 0.5547
+relationship: Other-relative, Count: 212
+Precision: 1.0000 | Recall: 0.1000 | F1: 0.1818
+relationship: Own-child, Count: 998
+Precision: 1.0000 | Recall: 0.4444 | F1: 0.6154
+relationship: Unmarried, Count: 707
+Precision: 0.9444 | Recall: 0.3269 | F1: 0.4857
+relationship: Wife, Count: 303
+Precision: 0.7419 | Recall: 0.6389 | F1: 0.6866
+race: Amer-Indian-Eskimo, Count: 53
+Precision: 0.3333 | Recall: 0.1667 | F1: 0.2222
+race: Asian-Pac-Islander, Count: 224
+Precision: 0.7333 | Recall: 0.6600 | F1: 0.6947
+race: Black, Count: 670
+Precision: 0.7895 | Recall: 0.5625 | F1: 0.6569
+race: Other, Count: 47
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+race: White, Count: 5,518
+Precision: 0.7453 | Recall: 0.6352 | F1: 0.6858
+sex: Female, Count: 2,196
+Precision: 0.7738 | Recall: 0.5221 | F1: 0.6235
+sex: Male, Count: 4,316
+Precision: 0.7412 | Recall: 0.6513 | F1: 0.6933
+native-country: ?, Count: 121
+Precision: 0.7692 | Recall: 0.7407 | F1: 0.7547
+native-country: Cambodia, Count: 4
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Canada, Count: 26
+Precision: 0.8571 | Recall: 0.8571 | F1: 0.8571
+native-country: China, Count: 17
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Columbia, Count: 17
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Cuba, Count: 12
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Dominican-Republic, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: England, Count: 19
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: France, Count: 6
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Germany, Count: 30
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Greece, Count: 10
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Guatemala, Count: 15
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.6667 | Recall: 0.7500 | F1: 0.7059
+native-country: Iran, Count: 11
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Ireland, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.3333 | Recall: 0.2500 | F1: 0.2857
+native-country: Jamaica, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Laos, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 125
+Precision: 0.8333 | Recall: 0.5556 | F1: 0.6667
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 47
+Precision: 0.8889 | Recall: 0.6154 | F1: 0.7273
+native-country: Poland, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 20
+Precision: 0.2500 | Recall: 0.3333 | F1: 0.2857
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,806
+Precision: 0.7448 | Recall: 0.6257 | F1: 0.6801
+native-country: Vietnam, Count: 15
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+workclass: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+workclass: Federal-gov, Count: 191
+Precision: 0.7571 | Recall: 0.7571 | F1: 0.7571
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6545 | F1: 0.6990
+workclass: Private, Count: 4,578
+Precision: 0.7447 | Recall: 0.6324 | F1: 0.6840
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7565 | Recall: 0.7373 | F1: 0.7468
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7339 | Recall: 0.5096 | F1: 0.6015
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6575 | F1: 0.7007
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.4286 | Recall: 0.2500 | F1: 0.3158
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.6923 | Recall: 0.5745 | F1: 0.6279
+education: Assoc-voc, Count: 273
+Precision: 0.6667 | Recall: 0.5397 | F1: 0.5965
+education: Bachelors, Count: 1,053
+Precision: 0.7564 | Recall: 0.7244 | F1: 0.7401
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6622 | Recall: 0.4261 | F1: 0.5185
+education: Masters, Count: 369
+Precision: 0.8309 | Recall: 0.8309 | F1: 0.8309
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8191 | Recall: 0.9167 | F1: 0.8652
+education: Some-college, Count: 1,485
+Precision: 0.6952 | Recall: 0.5271 | F1: 0.5996
+marital-status: Divorced, Count: 920
+Precision: 0.7826 | Recall: 0.3495 | F1: 0.4832
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7377 | Recall: 0.6816 | F1: 0.7085
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8182 | Recall: 0.4369 | F1: 0.5696
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+occupation: Adm-clerical, Count: 726
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6641 | Recall: 0.4696 | F1: 0.5502
+occupation: Exec-managerial, Count: 838
+Precision: 0.8011 | Recall: 0.7506 | F1: 0.7750
+occupation: Farming-fishing, Count: 193
+Precision: 0.6364 | Recall: 0.2500 | F1: 0.3590
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5938 | Recall: 0.4043 | F1: 0.4810
+occupation: Other-service, Count: 667
+Precision: 0.8571 | Recall: 0.2308 | F1: 0.3636
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7882 | Recall: 0.7500 | F1: 0.7686
+occupation: Protective-serv, Count: 136
+Precision: 0.6970 | Recall: 0.5476 | F1: 0.6133
+occupation: Sales, Count: 729
+Precision: 0.7333 | Recall: 0.6875 | F1: 0.7097
+occupation: Tech-support, Count: 189
+Precision: 0.6744 | Recall: 0.5686 | F1: 0.6170
+occupation: Transport-moving, Count: 317
+Precision: 0.6000 | Recall: 0.4219 | F1: 0.4954
+relationship: Husband, Count: 2,590
+Precision: 0.7401 | Recall: 0.6838 | F1: 0.7108
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8021 | Recall: 0.4096 | F1: 0.5423
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9231 | Recall: 0.2667 | F1: 0.4138
+relationship: Wife, Count: 322
+Precision: 0.7164 | Recall: 0.6713 | F1: 0.6931
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6250 | Recall: 0.5000 | F1: 0.5556
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7547 | Recall: 0.6452 | F1: 0.6957
+race: Black, Count: 599
+Precision: 0.7692 | Recall: 0.6154 | F1: 0.6838
+race: Other, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: White, Count: 5,595
+Precision: 0.7448 | Recall: 0.6317 | F1: 0.6836
+sex: Female, Count: 2,126
+Precision: 0.7267 | Recall: 0.5021 | F1: 0.5939
+sex: Male, Count: 4,387
+Precision: 0.7476 | Recall: 0.6532 | F1: 0.6972
+native-country: ?, Count: 125
+Precision: 0.7097 | Recall: 0.7097 | F1: 0.7097
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 22
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 0.8750 | F1: 0.9333
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8333 | Recall: 0.7692 | F1: 0.8000
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.7778 | Recall: 0.8750 | F1: 0.8235
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6250 | F1: 0.7692
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7448 | Recall: 0.6265 | F1: 0.6805
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 368
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+workclass: Federal-gov, Count: 186
+Precision: 0.7424 | Recall: 0.6447 | F1: 0.6901
+workclass: Local-gov, Count: 475
+Precision: 0.6912 | Recall: 0.6714 | F1: 0.6812
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,513
+Precision: 0.6974 | Recall: 0.6033 | F1: 0.6469
+workclass: Self-emp-inc, Count: 231
+Precision: 0.8319 | Recall: 0.7557 | F1: 0.7920
+workclass: Self-emp-not-inc, Count: 480
+Precision: 0.6598 | Recall: 0.5039 | F1: 0.5714
+workclass: State-gov, Count: 256
+Precision: 0.7188 | Recall: 0.7302 | F1: 0.7244
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 197
+Precision: 0.5000 | Recall: 0.0833 | F1: 0.1429
+education: 11th, Count: 235
+Precision: 0.8000 | Recall: 0.2857 | F1: 0.4211
+education: 12th, Count: 77
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 1st-4th, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 70
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 118
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 9th, Count: 84
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 225
+Precision: 0.6078 | Recall: 0.5741 | F1: 0.5905
+education: Assoc-voc, Count: 251
+Precision: 0.6341 | Recall: 0.4194 | F1: 0.5049
+education: Bachelors, Count: 1,071
+Precision: 0.7460 | Recall: 0.7600 | F1: 0.7529
+education: Doctorate, Count: 83
+Precision: 0.7812 | Recall: 0.8333 | F1: 0.8065
+education: HS-grad, Count: 2,157
+Precision: 0.5520 | Recall: 0.4220 | F1: 0.4783
+education: Masters, Count: 351
+Precision: 0.7949 | Recall: 0.7908 | F1: 0.7928
+education: Preschool, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: Prof-school, Count: 117
+Precision: 0.8696 | Recall: 0.9091 | F1: 0.8889
+education: Some-college, Count: 1,434
+Precision: 0.6761 | Recall: 0.4898 | F1: 0.5680
+marital-status: Divorced, Count: 853
+Precision: 0.8108 | Recall: 0.3448 | F1: 0.4839
+marital-status: Married-AF-spouse, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+marital-status: Married-civ-spouse, Count: 3,044
+Precision: 0.6967 | Recall: 0.6535 | F1: 0.6744
+marital-status: Married-spouse-absent, Count: 74
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+marital-status: Never-married, Count: 2,124
+Precision: 0.9070 | Recall: 0.3861 | F1: 0.5417
+marital-status: Separated, Count: 224
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+marital-status: Widowed, Count: 191
+Precision: 0.8889 | Recall: 0.4706 | F1: 0.6154
+occupation: ?, Count: 369
+Precision: 0.6786 | Recall: 0.4318 | F1: 0.5278
+occupation: Adm-clerical, Count: 768
+Precision: 0.6364 | Recall: 0.4949 | F1: 0.5568
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 793
+Precision: 0.6000 | Recall: 0.4693 | F1: 0.5266
+occupation: Exec-managerial, Count: 806
+Precision: 0.7886 | Recall: 0.7500 | F1: 0.7688
+occupation: Farming-fishing, Count: 196
+Precision: 0.6000 | Recall: 0.3750 | F1: 0.4615
+occupation: Handlers-cleaners, Count: 271
+Precision: 0.6667 | Recall: 0.1053 | F1: 0.1818
+occupation: Machine-op-inspct, Count: 392
+Precision: 0.5789 | Recall: 0.2245 | F1: 0.3235
+occupation: Other-service, Count: 659
+Precision: 0.6667 | Recall: 0.1538 | F1: 0.2500
+occupation: Priv-house-serv, Count: 36
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 855
+Precision: 0.7454 | Recall: 0.7358 | F1: 0.7405
+occupation: Protective-serv, Count: 148
+Precision: 0.6170 | Recall: 0.6444 | F1: 0.6304
+occupation: Sales, Count: 686
+Precision: 0.6570 | Recall: 0.6175 | F1: 0.6366
+occupation: Tech-support, Count: 178
+Precision: 0.7037 | Recall: 0.7917 | F1: 0.7451
+occupation: Transport-moving, Count: 353
+Precision: 0.6122 | Recall: 0.3947 | F1: 0.4800
+relationship: Husband, Count: 2,652
+Precision: 0.7039 | Recall: 0.6520 | F1: 0.6770
+relationship: Not-in-family, Count: 1,639
+Precision: 0.8659 | Recall: 0.4176 | F1: 0.5635
+relationship: Other-relative, Count: 199
+Precision: 0.5000 | Recall: 0.1667 | F1: 0.2500
+relationship: Own-child, Count: 1,000
+Precision: 0.7500 | Recall: 0.2143 | F1: 0.3333
+relationship: Unmarried, Count: 673
+Precision: 0.7857 | Recall: 0.3143 | F1: 0.4490
+relationship: Wife, Count: 349
+Precision: 0.6564 | Recall: 0.6859 | F1: 0.6708
+race: Amer-Indian-Eskimo, Count: 67
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 205
+Precision: 0.7561 | Recall: 0.5536 | F1: 0.6392
+race: Black, Count: 600
+Precision: 0.6078 | Recall: 0.4366 | F1: 0.5082
+race: Other, Count: 52
+Precision: 0.6667 | Recall: 0.4000 | F1: 0.5000
+race: White, Count: 5,588
+Precision: 0.7102 | Recall: 0.6295 | F1: 0.6674
+sex: Female, Count: 2,195
+Precision: 0.6869 | Recall: 0.5738 | F1: 0.6253
+sex: Male, Count: 4,317
+Precision: 0.7124 | Recall: 0.6241 | F1: 0.6653
+native-country: ?, Count: 106
+Precision: 0.8182 | Recall: 0.6000 | F1: 0.6923
+native-country: Cambodia, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 26
+Precision: 0.7500 | Recall: 0.3750 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+native-country: Columbia, Count: 12
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 27
+Precision: 0.8750 | Recall: 0.7778 | F1: 0.8235
+native-country: Dominican-Republic, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: El-Salvador, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 20
+Precision: 0.7500 | Recall: 0.4286 | F1: 0.5455
+native-country: France, Count: 6
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Germany, Count: 20
+Precision: 0.6667 | Recall: 0.2857 | F1: 0.4000
+native-country: Greece, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Guatemala, Count: 10
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 12
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 2
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: India, Count: 24
+Precision: 0.8571 | Recall: 0.6667 | F1: 0.7500
+native-country: Iran, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Ireland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 11
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Jamaica, Count: 21
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 9
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 120
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Nicaragua, Count: 6
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 41
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+native-country: Poland, Count: 13
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Puerto-Rico, Count: 19
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Scotland, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: South, Count: 17
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: United-States, Count: 5,856
+Precision: 0.7068 | Recall: 0.6225 | F1: 0.6619
+native-country: Vietnam, Count: 13
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+workclass: ?, Count: 357
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+workclass: Federal-gov, Count: 195
+Precision: 0.7179 | Recall: 0.7887 | F1: 0.7517
+workclass: Local-gov, Count: 395
+Precision: 0.6939 | Recall: 0.5812 | F1: 0.6326
+workclass: Never-worked, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,566
+Precision: 0.7378 | Recall: 0.6123 | F1: 0.6692
+workclass: Self-emp-inc, Count: 225
+Precision: 0.7826 | Recall: 0.7500 | F1: 0.7660
+workclass: Self-emp-not-inc, Count: 514
+Precision: 0.7475 | Recall: 0.5000 | F1: 0.5992
+workclass: State-gov, Count: 255
+Precision: 0.7273 | Recall: 0.7089 | F1: 0.7179
+workclass: Without-pay, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 180
+Precision: 0.7500 | Recall: 0.5000 | F1: 0.6000
+education: 11th, Count: 242
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: 12th, Count: 85
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 1st-4th, Count: 42
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 72
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 160
+Precision: 0.5000 | Recall: 0.1000 | F1: 0.1667
+education: 9th, Count: 110
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 199
+Precision: 0.6889 | Recall: 0.5962 | F1: 0.6392
+education: Assoc-voc, Count: 283
+Precision: 0.8333 | Recall: 0.5233 | F1: 0.6429
+education: Bachelors, Count: 1,073
+Precision: 0.7332 | Recall: 0.7528 | F1: 0.7429
+education: Doctorate, Count: 85
+Precision: 0.7536 | Recall: 0.8966 | F1: 0.8189
+education: HS-grad, Count: 2,063
+Precision: 0.5864 | Recall: 0.3601 | F1: 0.4462
+education: Masters, Count: 329
+Precision: 0.8218 | Recall: 0.8034 | F1: 0.8125
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 120
+Precision: 0.9184 | Recall: 0.9677 | F1: 0.9424
+education: Some-college, Count: 1,458
+Precision: 0.6887 | Recall: 0.5069 | F1: 0.5840
+marital-status: Divorced, Count: 899
+Precision: 0.6512 | Recall: 0.3218 | F1: 0.4308
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 3,011
+Precision: 0.7336 | Recall: 0.6603 | F1: 0.6950
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+marital-status: Never-married, Count: 2,100
+Precision: 0.7800 | Recall: 0.4149 | F1: 0.5417
+marital-status: Separated, Count: 215
+Precision: 1.0000 | Recall: 0.4167 | F1: 0.5882
+marital-status: Widowed, Count: 207
+Precision: 1.0000 | Recall: 0.2222 | F1: 0.3636
+occupation: ?, Count: 359
+Precision: 0.6250 | Recall: 0.4444 | F1: 0.5195
+occupation: Adm-clerical, Count: 755
+Precision: 0.6076 | Recall: 0.4615 | F1: 0.5246
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 849
+Precision: 0.6718 | Recall: 0.4378 | F1: 0.5301
+occupation: Exec-managerial, Count: 808
+Precision: 0.7836 | Recall: 0.7467 | F1: 0.7647
+occupation: Farming-fishing, Count: 201
+Precision: 0.7143 | Recall: 0.2273 | F1: 0.3448
+occupation: Handlers-cleaners, Count: 297
+Precision: 0.6667 | Recall: 0.2353 | F1: 0.3478
+occupation: Machine-op-inspct, Count: 408
+Precision: 0.6316 | Recall: 0.2400 | F1: 0.3478
+occupation: Other-service, Count: 626
+Precision: 0.7500 | Recall: 0.1200 | F1: 0.2069
+occupation: Priv-house-serv, Count: 32
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8000 | Recall: 0.7890 | F1: 0.7945
+occupation: Protective-serv, Count: 129
+Precision: 0.5714 | Recall: 0.5405 | F1: 0.5556
+occupation: Sales, Count: 729
+Precision: 0.6989 | Recall: 0.6468 | F1: 0.6718
+occupation: Tech-support, Count: 190
+Precision: 0.6721 | Recall: 0.6721 | F1: 0.6721
+occupation: Transport-moving, Count: 311
+Precision: 0.7059 | Recall: 0.4211 | F1: 0.5275
+relationship: Husband, Count: 2,694
+Precision: 0.7378 | Recall: 0.6656 | F1: 0.6998
+relationship: Not-in-family, Count: 1,648
+Precision: 0.7342 | Recall: 0.3742 | F1: 0.4957
+relationship: Other-relative, Count: 208
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+relationship: Own-child, Count: 969
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Unmarried, Count: 708
+Precision: 0.7500 | Recall: 0.3000 | F1: 0.4286
+relationship: Wife, Count: 285
+Precision: 0.7031 | Recall: 0.6294 | F1: 0.6642
+race: Amer-Indian-Eskimo, Count: 55
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+race: Asian-Pac-Islander, Count: 210
+Precision: 0.6383 | Recall: 0.5769 | F1: 0.6061
+race: Black, Count: 634
+Precision: 0.7581 | Recall: 0.5875 | F1: 0.6620
+race: Other, Count: 50
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+race: White, Count: 5,563
+Precision: 0.7378 | Recall: 0.6217 | F1: 0.6748
+sex: Female, Count: 2,124
+Precision: 0.7099 | Recall: 0.5251 | F1: 0.6037
+sex: Male, Count: 4,388
+Precision: 0.7381 | Recall: 0.6331 | F1: 0.6816
+native-country: ?, Count: 104
+Precision: 0.5714 | Recall: 0.6000 | F1: 0.5854
+native-country: Cambodia, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 25
+Precision: 0.7143 | Recall: 0.5556 | F1: 0.6250
+native-country: China, Count: 10
+Precision: 0.3333 | Recall: 1.0000 | F1: 0.5000
+native-country: Columbia, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 21
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Dominican-Republic, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 25
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: England, Count: 18
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: France, Count: 7
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Germany, Count: 27
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+native-country: Greece, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Holand-Netherlands, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 15
+Precision: 0.6250 | Recall: 0.6250 | F1: 0.6250
+native-country: Iran, Count: 9
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Ireland, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 20
+Precision: 1.0000 | Recall: 0.5714 | F1: 0.7273
+native-country: Jamaica, Count: 14
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 16
+Precision: 0.5000 | Recall: 0.6667 | F1: 0.5714
+native-country: Laos, Count: 4
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Mexico, Count: 152
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+native-country: Nicaragua, Count: 5
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Outlying-US(Guam-USVI-etc), Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 37
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Poland, Count: 8
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Portugal, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 12
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Taiwan, Count: 14
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,818
+Precision: 0.7428 | Recall: 0.6205 | F1: 0.6762
+native-country: Vietnam, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 345
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+workclass: Federal-gov, Count: 200
+Precision: 0.7973 | Recall: 0.7375 | F1: 0.7662
+workclass: Local-gov, Count: 421
+Precision: 0.7434 | Recall: 0.6667 | F1: 0.7029
+workclass: Never-worked, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,547
+Precision: 0.7450 | Recall: 0.5990 | F1: 0.6641
+workclass: Self-emp-inc, Count: 214
+Precision: 0.7769 | Recall: 0.8211 | F1: 0.7984
+workclass: Self-emp-not-inc, Count: 511
+Precision: 0.6667 | Recall: 0.5035 | F1: 0.5737
+workclass: State-gov, Count: 270
+Precision: 0.8364 | Recall: 0.6866 | F1: 0.7541
+workclass: Without-pay, Count: 3
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 10th, Count: 184
+Precision: 0.5000 | Recall: 0.1538 | F1: 0.2353
+education: 11th, Count: 240
+Precision: 1.0000 | Recall: 0.3636 | F1: 0.5333
+education: 12th, Count: 86
+Precision: 1.0000 | Recall: 0.1429 | F1: 0.2500
+education: 1st-4th, Count: 43
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+education: 5th-6th, Count: 67
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+education: 7th-8th, Count: 104
+Precision: 0.6000 | Recall: 0.5000 | F1: 0.5455
+education: 9th, Count: 100
+Precision: 1.0000 | Recall: 0.2000 | F1: 0.3333
+education: Assoc-acdm, Count: 205
+Precision: 0.7561 | Recall: 0.5849 | F1: 0.6596
+education: Assoc-voc, Count: 292
+Precision: 0.6667 | Recall: 0.5600 | F1: 0.6087
+education: Bachelors, Count: 1,068
+Precision: 0.7864 | Recall: 0.7522 | F1: 0.7689
+education: Doctorate, Count: 75
+Precision: 0.8548 | Recall: 0.9298 | F1: 0.8908
+education: HS-grad, Count: 2,105
+Precision: 0.6771 | Recall: 0.3746 | F1: 0.4824
+education: Masters, Count: 319
+Precision: 0.8034 | Recall: 0.8034 | F1: 0.8034
+education: Preschool, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 112
+Precision: 0.8471 | Recall: 0.9474 | F1: 0.8944
+education: Some-college, Count: 1,501
+Precision: 0.6481 | Recall: 0.5336 | F1: 0.5853
+marital-status: Divorced, Count: 869
+Precision: 0.7895 | Recall: 0.3448 | F1: 0.4800
+marital-status: Married-AF-spouse, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,996
+Precision: 0.7412 | Recall: 0.6679 | F1: 0.7026
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2222 | F1: 0.3333
+marital-status: Never-married, Count: 2,171
+Precision: 0.8718 | Recall: 0.3542 | F1: 0.5037
+marital-status: Separated, Count: 186
+Precision: 1.0000 | Recall: 0.3571 | F1: 0.5263
+marital-status: Widowed, Count: 187
+Precision: 1.0000 | Recall: 0.3077 | F1: 0.4706
+occupation: ?, Count: 346
+Precision: 0.7308 | Recall: 0.5278 | F1: 0.6129
+occupation: Adm-clerical, Count: 791
+Precision: 0.6495 | Recall: 0.5431 | F1: 0.5915
+occupation: Armed-Forces, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 820
+Precision: 0.5909 | Recall: 0.4358 | F1: 0.5016
+occupation: Exec-managerial, Count: 791
+Precision: 0.8072 | Recall: 0.7792 | F1: 0.7929
+occupation: Farming-fishing, Count: 203
+Precision: 0.7857 | Recall: 0.4783 | F1: 0.5946
+occupation: Handlers-cleaners, Count: 288
+Precision: 0.6667 | Recall: 0.1739 | F1: 0.2759
+occupation: Machine-op-inspct, Count: 427
+Precision: 0.6552 | Recall: 0.3725 | F1: 0.4750
+occupation: Other-service, Count: 647
+Precision: 0.8333 | Recall: 0.1562 | F1: 0.2632
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 817
+Precision: 0.8128 | Recall: 0.7951 | F1: 0.8039
+occupation: Protective-serv, Count: 130
+Precision: 0.7353 | Recall: 0.5814 | F1: 0.6494
+occupation: Sales, Count: 739
+Precision: 0.6918 | Recall: 0.5288 | F1: 0.5994
+occupation: Tech-support, Count: 180
+Precision: 0.7143 | Recall: 0.6250 | F1: 0.6667
+occupation: Transport-moving, Count: 306
+Precision: 0.6000 | Recall: 0.2679 | F1: 0.3704
+relationship: Husband, Count: 2,631
+Precision: 0.7392 | Recall: 0.6661 | F1: 0.7008
+relationship: Not-in-family, Count: 1,650
+Precision: 0.8451 | Recall: 0.3488 | F1: 0.4938
+relationship: Other-relative, Count: 184
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,082
+Precision: 1.0000 | Recall: 0.2941 | F1: 0.4545
+relationship: Unmarried, Count: 656
+Precision: 0.7857 | Recall: 0.3056 | F1: 0.4400
+relationship: Wife, Count: 309
+Precision: 0.7434 | Recall: 0.7107 | F1: 0.7267
+race: Amer-Indian-Eskimo, Count: 65
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+race: Asian-Pac-Islander, Count: 207
+Precision: 0.7273 | Recall: 0.5714 | F1: 0.6400
+race: Black, Count: 621
+Precision: 0.8333 | Recall: 0.5495 | F1: 0.6623
+race: Other, Count: 67
+Precision: 0.6667 | Recall: 0.5714 | F1: 0.6154
+race: White, Count: 5,552
+Precision: 0.7450 | Recall: 0.6301 | F1: 0.6828
+sex: Female, Count: 2,130
+Precision: 0.7622 | Recall: 0.5851 | F1: 0.6620
+sex: Male, Count: 4,382
+Precision: 0.7451 | Recall: 0.6289 | F1: 0.6821
+native-country: ?, Count: 127
+Precision: 0.8065 | Recall: 0.6579 | F1: 0.7246
+native-country: Cambodia, Count: 2
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Canada, Count: 22
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+native-country: China, Count: 15
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+native-country: Columbia, Count: 11
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 16
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Dominican-Republic, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: El-Salvador, Count: 24
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 19
+Precision: 0.8750 | Recall: 0.7000 | F1: 0.7778
+native-country: France, Count: 5
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Germany, Count: 28
+Precision: 1.0000 | Recall: 0.5556 | F1: 0.7143
+native-country: Greece, Count: 7
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.7000 | Recall: 1.0000 | F1: 0.8235
+native-country: Iran, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Italy, Count: 14
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+native-country: Jamaica, Count: 19
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.7143 | F1: 0.8333
+native-country: Laos, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 132
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+native-country: Nicaragua, Count: 9
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 38
+Precision: 0.5556 | Recall: 0.5000 | F1: 0.5263
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Portugal, Count: 8
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Puerto-Rico, Count: 19
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: South, Count: 18
+Precision: 0.5000 | Recall: 0.3333 | F1: 0.4000
+native-country: Taiwan, Count: 8
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: United-States, Count: 5,820
+Precision: 0.7438 | Recall: 0.6265 | F1: 0.6801
+native-country: Vietnam, Count: 14
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 377
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+workclass: Federal-gov, Count: 188
+Precision: 0.8136 | Recall: 0.6486 | F1: 0.7218
+workclass: Local-gov, Count: 415
+Precision: 0.7451 | Recall: 0.6129 | F1: 0.6726
+workclass: Never-worked, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 4,492
+Precision: 0.7312 | Recall: 0.6145 | F1: 0.6678
+workclass: Self-emp-inc, Count: 234
+Precision: 0.8222 | Recall: 0.8538 | F1: 0.8377
+workclass: Self-emp-not-inc, Count: 538
+Precision: 0.7607 | Recall: 0.5973 | F1: 0.6692
+workclass: State-gov, Count: 263
+Precision: 0.7705 | Recall: 0.6620 | F1: 0.7121
+workclass: Without-pay, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 189
+Precision: 1.0000 | Recall: 0.0769 | F1: 0.1429
+education: 11th, Count: 233
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+education: 12th, Count: 87
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+education: 1st-4th, Count: 31
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 123
+Precision: 0.5000 | Recall: 0.1429 | F1: 0.2222
+education: 9th, Count: 105
+Precision: 1.0000 | Recall: 0.1250 | F1: 0.2222
+education: Assoc-acdm, Count: 240
+Precision: 0.6275 | Recall: 0.5424 | F1: 0.5818
+education: Assoc-voc, Count: 283
+Precision: 0.6774 | Recall: 0.5600 | F1: 0.6131
+education: Bachelors, Count: 1,090
+Precision: 0.7551 | Recall: 0.7620 | F1: 0.7585
+education: Doctorate, Count: 93
+Precision: 0.8873 | Recall: 0.8514 | F1: 0.8690
+education: HS-grad, Count: 2,091
+Precision: 0.6590 | Recall: 0.4145 | F1: 0.5089
+education: Masters, Count: 355
+Precision: 0.8564 | Recall: 0.8350 | F1: 0.8456
+education: Preschool, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 111
+Precision: 0.8571 | Recall: 0.8780 | F1: 0.8675
+education: Some-college, Count: 1,413
+Precision: 0.6582 | Recall: 0.5265 | F1: 0.5850
+marital-status: Divorced, Count: 902
+Precision: 0.7857 | Recall: 0.3333 | F1: 0.4681
+marital-status: Married-AF-spouse, Count: 6
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,975
+Precision: 0.7359 | Recall: 0.6762 | F1: 0.7048
+marital-status: Married-spouse-absent, Count: 76
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+marital-status: Never-married, Count: 2,162
+Precision: 0.9020 | Recall: 0.4742 | F1: 0.6216
+marital-status: Separated, Count: 191
+Precision: 1.0000 | Recall: 0.2308 | F1: 0.3750
+marital-status: Widowed, Count: 200
+Precision: 0.8333 | Recall: 0.2778 | F1: 0.4167
+occupation: ?, Count: 380
+Precision: 0.3750 | Recall: 0.2500 | F1: 0.3000
+occupation: Adm-clerical, Count: 730
+Precision: 0.6538 | Recall: 0.5543 | F1: 0.6000
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 816
+Precision: 0.6165 | Recall: 0.4339 | F1: 0.5093
+occupation: Exec-managerial, Count: 823
+Precision: 0.7805 | Recall: 0.8060 | F1: 0.7931
+occupation: Farming-fishing, Count: 201
+Precision: 0.8000 | Recall: 0.4615 | F1: 0.5854
+occupation: Handlers-cleaners, Count: 241
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+occupation: Machine-op-inspct, Count: 397
+Precision: 0.5294 | Recall: 0.3396 | F1: 0.4138
+occupation: Other-service, Count: 696
+Precision: 0.8571 | Recall: 0.2143 | F1: 0.3429
+occupation: Priv-house-serv, Count: 29
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 823
+Precision: 0.7943 | Recall: 0.7943 | F1: 0.7943
+occupation: Protective-serv, Count: 106
+Precision: 0.8276 | Recall: 0.5455 | F1: 0.6575
+occupation: Sales, Count: 767
+Precision: 0.7576 | Recall: 0.5787 | F1: 0.6562
+occupation: Tech-support, Count: 191
+Precision: 0.8140 | Recall: 0.5224 | F1: 0.6364
+occupation: Transport-moving, Count: 310
+Precision: 0.6341 | Recall: 0.3881 | F1: 0.4815
+relationship: Husband, Count: 2,626
+Precision: 0.7342 | Recall: 0.6802 | F1: 0.7062
+relationship: Not-in-family, Count: 1,666
+Precision: 0.8353 | Recall: 0.4152 | F1: 0.5547
+relationship: Other-relative, Count: 212
+Precision: 1.0000 | Recall: 0.1000 | F1: 0.1818
+relationship: Own-child, Count: 998
+Precision: 1.0000 | Recall: 0.4444 | F1: 0.6154
+relationship: Unmarried, Count: 707
+Precision: 0.9444 | Recall: 0.3269 | F1: 0.4857
+relationship: Wife, Count: 303
+Precision: 0.7419 | Recall: 0.6389 | F1: 0.6866
+race: Amer-Indian-Eskimo, Count: 53
+Precision: 0.3333 | Recall: 0.1667 | F1: 0.2222
+race: Asian-Pac-Islander, Count: 224
+Precision: 0.7333 | Recall: 0.6600 | F1: 0.6947
+race: Black, Count: 670
+Precision: 0.7895 | Recall: 0.5625 | F1: 0.6569
+race: Other, Count: 47
+Precision: 0.6000 | Recall: 0.7500 | F1: 0.6667
+race: White, Count: 5,518
+Precision: 0.7453 | Recall: 0.6352 | F1: 0.6858
+sex: Female, Count: 2,196
+Precision: 0.7738 | Recall: 0.5221 | F1: 0.6235
+sex: Male, Count: 4,316
+Precision: 0.7412 | Recall: 0.6513 | F1: 0.6933
+native-country: ?, Count: 121
+Precision: 0.7692 | Recall: 0.7407 | F1: 0.7547
+native-country: Cambodia, Count: 4
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Canada, Count: 26
+Precision: 0.8571 | Recall: 0.8571 | F1: 0.8571
+native-country: China, Count: 17
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Columbia, Count: 17
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Cuba, Count: 12
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
+native-country: Dominican-Republic, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Ecuador, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: England, Count: 19
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: France, Count: 6
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Germany, Count: 30
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Greece, Count: 10
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Guatemala, Count: 15
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Haiti, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: India, Count: 20
+Precision: 0.6667 | Recall: 0.7500 | F1: 0.7059
+native-country: Iran, Count: 11
+Precision: 0.7500 | Recall: 1.0000 | F1: 0.8571
+native-country: Ireland, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.3333 | Recall: 0.2500 | F1: 0.2857
+native-country: Jamaica, Count: 14
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Japan, Count: 13
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Laos, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 125
+Precision: 0.8333 | Recall: 0.5556 | F1: 0.6667
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 47
+Precision: 0.8889 | Recall: 0.6154 | F1: 0.7273
+native-country: Poland, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 27
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Scotland, Count: 1
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 20
+Precision: 0.2500 | Recall: 0.3333 | F1: 0.2857
+native-country: Taiwan, Count: 9
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,806
+Precision: 0.7448 | Recall: 0.6257 | F1: 0.6801
+native-country: Vietnam, Count: 15
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 6
+Precision: 0.6667 | Recall: 0.6667 | F1: 0.6667
diff --git a/test_ml.py b/test_ml.py
index 5f8306f14c..7ac4563e08 100644
--- a/test_ml.py
+++ b/test_ml.py
@@ -1,28 +1,94 @@
 import pytest
 # TODO: add necessary import
+from ml.model import train_and_save_final_model
+from ml.model import inference
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+import os
+import pandas as pd
+import tempfile
+
+
 
 # TODO: implement the first test. Change the function name and input as needed
-def test_one():
+#sample data
+@pytest.fixture
+def fake_sample_data():
+    return pd.DataFrame({
+        'feature1': ['A', 'B','C'],
+        'feature2': ["X", "Y", 'Z'],
+        'label': [1, 0, 1]
+    })
+
+
+def test_train_and_save_final_model(fake_sample_data):
+    
     """
     # add description for the first test
+    test if the pipeline runs.
     """
     # Your code here
+    with tempfile.TemporaryDirectory()as tmp_dir:
+        model, encoder, lb = train_and_save_final_model(
+            data=fake_sample_data,
+            categorical_features=["feature1", "feature2"],
+            label='label',
+            model_dir=tmp_dir
+        )
+        #check output is not none
+        assert model is not None
+        assert encoder is not None
+        assert lb is not None
     pass
 
+#Effective Python Testing With pytest, Dane Hillard,Dec 08, 2024, https://realpython.com/pytest-python-testing/
+
 
 # TODO: implement the second test. Change the function name and input as needed
-def test_two():
+def test_function_inference():
     """
     # add description for the second test
+    test function def inference(model, X):
     """
     # Your code here
+    
+    #simple model to train, look for prediction 
+    model = RandomForestClassifier().fit(
+        pd.DataFrame({"a": [0,1], "b": [1, 0]}),
+        [0,1]
+    )
+    
+    preds = model.predict(pd.DataFrame({"a": [1], "b": [0]}))
+    
+    #assert what should be true
+    assert preds[0] in [0, 1]
     pass
 
 
 # TODO: implement the third test. Change the function name and input as needed
-def test_three():
+def test_data_split_shape():
     """
     # add description for the third test
+    checks the shape of training and test datasets
     """
     # Your code here
+    #using the same sample dataset in test1
+    data = pd.DataFrame({
+        'feature1': ['A', 'B','C'],
+        'feature2': ["X", "Y", 'Z'],
+        'label': [1, 0, 1]
+    })
+    
+    #split and label
+    X=data[['feature1', 'feature2']]
+    y=data['label']
+
+    #train test split 1-train 2- split
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1, random_state=42)
+    
+    #check shapes
+    assert X_train.shape ==(2, 2)
+    assert X_test.shape ==(1, 2)
+    assert y_train.shape[0] == 2
+    assert y_test.shape[0] == 1
     pass
diff --git a/train_model.py b/train_model.py
index ae783ed5b9..cb99333a47 100644
--- a/train_model.py
+++ b/train_model.py
@@ -1,7 +1,10 @@
 import os
 
+from ml.model import train_and_save_final_model
 import pandas as pd
 from sklearn.model_selection import train_test_split
+from sklearn.model_selection import KFold
+import numpy as np
 
 from ml.data import process_data
 from ml.model import (
@@ -13,15 +16,18 @@
     train_model,
 )
 # TODO: load the cencus.csv data
-project_path = "Your path here"
+project_path = "/home/mha2112/Deploying-a-Scalable-ML-Pipeline-with-FastAPI"
 data_path = os.path.join(project_path, "data", "census.csv")
 print(data_path)
-data = None # your code here
+#data = None # your code here
+data = pd.read_csv(data_path)
 
 # TODO: split the provided data to have a train dataset and a test dataset
 # Optional enhancement, use K-fold cross validation instead of a train-test split.
-train, test = None, None# Your code here
-
+#X_train, X_test, y_train, y_test = train_test_split
+#train, test = None, None# Your code here
+#train, test = train_test_split(data, test_size=.20, random_state=42)
+###K-fold
 # DO NOT MODIFY
 cat_features = [
     "workclass",
@@ -34,54 +40,99 @@
     "native-country",
 ]
 
-# TODO: use the process_data function provided to process the data.
-X_train, y_train, encoder, lb = process_data(
-    # your code here
-    # use the train dataset 
-    # use training=True
-    # do not need to pass encoder and lb as input
+
+k=5
+kf = KFold(n_splits=k, shuffle=True, random_state=42)
+#some scores-precision_score, recall_scores, f1_scores
+precision_score=[]
+recall_scores=[]
+f1_scores=[]
+
+#train and eval
+for fold, (train_idx, test_idx) in enumerate(kf.split(data)):
+    train= data.iloc[train_idx]
+    test= data.iloc[test_idx]
+    
+    X_train, y_train, encoder, lb = process_data(
+        train,
+        categorical_features=cat_features,
+        label="salary",
+        training=True
+    )
+    
+    X_test, y_test, _, _ = process_data(
+        test,
+        categorical_features=cat_features,
+        label="salary",
+        training=False,
+        encoder=encoder,
+        lb=lb
     )
 
-X_test, y_test, _, _ = process_data(
-    test,
-    categorical_features=cat_features,
-    label="salary",
-    training=False,
-    encoder=encoder,
-    lb=lb,
-)
+
 
 # TODO: use the train_model function to train the model on the training dataset
-model = None # your code here
+#model = None # your code here
+    model = train_model(X_train, y_train)
 
 # save the model and the encoder
-model_path = os.path.join(project_path, "model", "model.pkl")
-save_model(model, model_path)
-encoder_path = os.path.join(project_path, "model", "encoder.pkl")
-save_model(encoder, encoder_path)
+#model_path = os.path.join(project_path, "model", "model.pkl")
+#save_model(model, model_path)
+#encoder_path = os.path.join(project_path, "model", "encoder.pkl")
+#save_model(encoder, encoder_path)
 
 # load the model
-model = load_model(
-    model_path
-) 
+#model = load_model(
+    #model_path
+#) 
 
 # TODO: use the inference function to run the model inferences on the test dataset.
-preds = None # your code here
+#preds = None # your code here
+    preds = inference(model, X_test)
+
 
 # Calculate and print the metrics
-p, r, fb = compute_model_metrics(y_test, preds)
-print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}")
+    p, r, fb = compute_model_metrics(y_test, preds)
+    precision_score.append(p)
+    recall_scores.append(r)
+    f1_scores.append(fb)
+    print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}")
+
+
+
 
 # TODO: compute the performance on model slices using the performance_on_categorical_slice function
 # iterate through the categorical features
-for col in cat_features:
+    for col in cat_features:
     # iterate through the unique values in one categorical feature
-    for slicevalue in sorted(test[col].unique()):
-        count = test[test[col] == slicevalue].shape[0]
-        p, r, fb = performance_on_categorical_slice(
+        for slicevalue in sorted(test[col].unique()):
+            count = test[test[col] == slicevalue].shape[0]
+            p, r, fb = performance_on_categorical_slice(
             # your code here
             # use test, col and slicevalue as part of the input
-        )
-        with open("slice_output.txt", "a") as f:
-            print(f"{col}: {slicevalue}, Count: {count:,}", file=f)
-            print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}", file=f)
+                data=test,
+                column_name=col,
+                slice_value=slicevalue,
+                categorical_features=cat_features,
+                label='salary',
+                encoder=encoder,
+                lb=lb,
+                model=model
+            )
+            with open("slice_output.txt", "a") as f:
+                print(f"{col}: {slicevalue}, Count: {count:,}", file=f)
+                print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}", file=f)
+
+#metrics for folds
+print(f"\n=== Average Metrics Across Folds ===")
+print(f"avg precision: {np.mean(precision_score): .4f}")
+print(f"avg recall: {np.mean(recall_scores): .4f}")
+print(f"avg f1_score: {np.mean(f1_scores): .4f}")
+
+#added because of k-fold model            
+final_model, encoder, lb = train_and_save_final_model(
+    data=data,
+    categorical_features=cat_features,
+    label="salary",
+    model_dir=os.path.join(project_path, "model")
+)
diff --git a/which b/which
new file mode 100644
index 0000000000..f30653b5ac
--- /dev/null
+++ b/which
@@ -0,0 +1,14 @@
+[?2004h[?1h=[?25l
+>>> [?12l[?25h[1@>[1@ [1@c[1@o[1@n[1@d[1@a[1@ [1@e[1@n[1@v[1@ [1@c[1@r[1@e[1@a[1@t[1@e[1@ [1@-[1@f[1@ [1@e[1@n[1@v[1@i[1@r[1@o[1@n[1@m[1@e[1@n[1@t[1@.[1@y[1@m[1@l
+
[?2004l[?1l>[?2004h[?1h=[?25l
+>>> [?12l[?25h[1@>[1@ [1@c[1@o[1@n[1@d[1@a[1@ [1@e[1@n[1@v[1@ [1@c[1@r[1@e[1@a[1@t[1@e[1@ [1@-[1@f[1@ [1@e[1@n[1@v[1@i[1@r[1@o[1@n[1@m[1@e[1@n[1@t[1@.[1@y[1@m[1@l
+
[?2004l[?1l>[?2004h[?1h=[?25l
+>>> [?12l[?25h[1@>[1@ [1@c[1@o[1@n[1@d[1@a[1@ [1@e[1@n[1@v[1@ [1@c[1@r[1@e[1@a[1@t[1@e[1@ [1@-[1@f[1@ [1@e[1@n[1@v[1@i[1@r[1@o[1@n[1@m[1@e[1@n[1@t[1@.[1@y[1@m[1@l
+
[?2004l[?1l>[?2004h[?1h=[?25l
+>>> [?12l[?25h[1@c[1@o[1@d[1@a[?25l[?12l[?25h[1@>[1@ [1@p[1@y[1@t[1@h[1@o[1@n[1@3[1@ [1@-[1@m[1@ [1@v[1@e[1@n[1@v[1@ [1@f[1@a[1@s[1@t[1@a[1@p[1@i
+
[?2004l[?1l>[?2004h[?1h=[?25l
+>>> [?12l[?25h[1@>[1@ [1@s[1@o[1@u[1@r[1@c[1@e[1@ [1@f[1@a[1@s[1@t[1@a[1@p[1@i[1@/[1@b[1@i[1@n[1@/[1@a[1@c[1@t[1@i[1@v[1@a[1@t[1@e
+
[?2004l[?1l>[?2004h[?1h=[?25l
+>>> [?12l[?25h
+
[?2004l[?1l>[?2004h[?1h=[?25l
+>>> [?12l[?25h[1@y[1@t[1@h[1@o[1@n[1@ [1@-[1@-[1@v[1@e[1@r[1@s[1@i[1@o[1@n
\ No newline at end of file