Merge pull request #99715 from likebupt/review-sample-docs

update screenshots in sample docs, correct some errors

Author: rmca14

articles/machine-learning/how-to-designer-sample-classification-churn.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-author: xiaoharper
-ms.author: zhanxia
+author: likebupt
+ms.author: keli19
 ms.reviewer: sgilley
-ms.date: 11/04/2019
+ms.date: 12/25/2019
 ---
 
 # Use boosted decision tree to predict churn with Azure Machine Learning designer
@@ -46,11 +46,11 @@ First, some simple data processing.
 
 - The raw dataset has many missing values. Use the **Clean Missing Data** module to replace the missing values with 0.
 
-    ![Clean the dataset](./media/how-to-designer-sample-classification-churn/cleaned-dataset.png)
+    ![Clean the dataset](media/how-to-designer-sample-classification-churn/sample5-dataset-1225.png)
 
 - The features and the corresponding churn are in different datasets. Use the **Add Columns** module to append the label columns to the feature columns. The first column, **Col1**, is the label column. From the visualization result we can see the dataset is unbalanced. There way more negative (-1) examples than positive examples (+1). We will use **SMOTE** module to increase underrepresented cases later.
 
-    ![Add the column dataset](./media/how-to-designer-sample-classification-churn/added-column1.png)
+    ![Add the column dataset](./media/how-to-designer-sample-classification-churn/sample5-addcol-1225.png)
 
 
 
@@ -64,7 +64,7 @@ First, some simple data processing.
 
 Visualize the output of the **Evaluate Model** module to see the performance of the model on the test set. 
 
-![Evaluate the results](./media/how-to-designer-sample-classification-churn/evaluate-result.png)
+![Evaluate the results](./media/how-to-designer-sample-classification-churn/sample5-evaluate-1225.png)
 
  You can move the **Threshold** slider and see the metrics change for the binary classification task. 
 

articles/machine-learning/how-to-designer-sample-classification-credit-risk-cost-sensitive.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-author: xiaoharper
-ms.author: zhanxia
+author: likebupt
+ms.author: keli19
 ms.reviewer: peterlu
-ms.date: 11/04/2019
+ms.date: 12/25/2019
 ---
 # Build a classifier & use Python scripts to predict credit risk using Azure Machine Learning designer
 
@@ -138,7 +138,7 @@ def azureml_main(dataframe1 = None, dataframe2 = None):
 
 To view the results of the pipeline, you can right-click the Visualize output of the last **Select Columns in Dataset** module.
 
-![Visualize output](./media/how-to-designer-sample-classification-credit-risk-cost-sensitive/result.png)
+![Visualize output](media/how-to-designer-sample-classification-credit-risk-cost-sensitive/sample4-lastselect-1225.png)
 
 The first column lists the machine learning algorithm used to generate the model.
 

articles/machine-learning/how-to-designer-sample-classification-flight-delay.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-author: xiaoharper
-ms.author: zhanxia
+author: likebupt
+ms.author: keli19
 ms.reviewer: peterlu
-ms.date: 11/04/2019
+ms.date: 12/25/2019
 ---
 # Build a classifier & use R to predict flight delays with Azure Machine Learning designer
 
@@ -109,7 +109,7 @@ Finally, to test the quality of the results, add the **Evaluate Model** module t
 ## Evaluate
 The logistic regression model has AUC of 0.631 on the test set.
 
- ![evaluate](./media/how-to-designer-sample-classification-flight-delay/evaluate.png)
+ ![evaluate](media/how-to-designer-sample-classification-flight-delay/sample6-evaluate-1225.png)
 
 ## Next steps
 

articles/machine-learning/how-to-designer-sample-classification-predict-income.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-author: xiaoharper
-ms.author: zhanxia
+author: likebupt
+ms.author: keli19
 ms.reviewer: peterlu
-ms.date: 11/04/2019
+ms.date: 12/25/2019
 ---
 # Build a classifier & use feature selection to predict income with Azure Machine Learning designer
 
@@ -19,7 +19,7 @@ ms.date: 11/04/2019
 
 Learn how to build a machine learning classifier without writing a single line of code using the designer (preview). This sample trains a **two-class boosted decision tree** to predict adult census income (>=50K or <=50K).
 
-Because the question is answering "Which one?" this is called a classification problem. However, you can apply the same fundamental process to tackle any type of machine learning problem whether it be regression, classification, clustering, and so on.
+Because the question is answering "Which one?" this is called a classification problem. However, you can apply the same fundamental process to tackle any type of machine learning problem - regression, classification, clustering, and so on.
 
 Here's the final pipeline graph for this sample:
 
@@ -36,7 +36,7 @@ Here's the final pipeline graph for this sample:
 ## Data
 
 The dataset contains 14 features and one label column. There are multiple types of features, including numerical and categorical. The following diagram shows an excerpt from the dataset:
-![data](./media/how-to-designer-sample-classification-predict-income/data.png)
+![data](media/how-to-designer-sample-classification-predict-income/sample3-dataset-1225.png)
 
 
 
@@ -60,7 +60,7 @@ Follow these steps to create the pipeline:
 
 ## Results
 
-![Evaluate the results](./media/how-to-designer-sample-classification-predict-income/evaluate-result.png)
+![Evaluate the results](media/how-to-designer-sample-classification-predict-income/sample3-evaluate-1225.png)
 
 In the evaluation results, you can see that the curves like ROC, Precision-recall and confusion metrics. 
 

articles/machine-learning/how-to-designer-sample-regression-automobile-price-basic.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-author: xiaoharper
-ms.author: zhanxia
+author: likebupt
+ms.author: keli19
 ms.reviewer: peterlu
-ms.date: 11/04/2019
+ms.date: 12/25/2019
 ---
 # Use regression to predict car prices with Azure Machine Learning designer
 
@@ -19,7 +19,7 @@ ms.date: 11/04/2019
 
 Learn how to build a machine learning regression model without writing a single line of code using the designer (preview).
 
-This pipeline trains a **decision forest regressor** to predict a car's price based on technical features such as make, model, horsepower, and size. Because you're trying to answer the question "How much?" this is called a regression problem. However, you can apply the same fundamental steps in this example to tackle any type of machine learning problem whether it be regression, classification, clustering, and so on.
+This pipeline trains a **linear regressor** to predict a car's price based on technical features such as make, model, horsepower, and size. Because you're trying to answer the question "How much?" this is called a regression problem. However, you can apply the same fundamental steps in this example to tackle any type of machine learning problem whether it be regression, classification, clustering, and so on.
 
 The fundamental steps of a training machine learning model are:
 
@@ -67,11 +67,11 @@ After the model is trained, you can use the **Score Model** and **Evaluate Model
 
 **Score Model** generates predictions for the test dataset by using the trained model. To check the result, select the output port of **Score Model** and then select **Visualize**.
 
-![Score result](./media/how-to-designer-sample-regression-automobile-price-basic/score-result.png)
+![Score result](./media/how-to-designer-sample-regression-automobile-price-basic/sample1-score-1225.png)
 
 Pass the scores to the **Evaluate Model** module to generate evaluation metrics. To check the result, select the output port of the **Evaluate Model** and then select **Visualize**.
 
-![Evaluate result](./media/how-to-designer-sample-regression-automobile-price-basic/evaluate-result.png)
+![Evaluate result](./media/how-to-designer-sample-regression-automobile-price-basic/sample1-evaluate-1225.png)
 
 ## Clean up resources
 

articles/machine-learning/how-to-designer-sample-regression-automobile-price-compare-algorithms.md

@@ -6,10 +6,10 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-author: xiaoharper
-ms.author: zhanxia
+author: likebupt
+ms.author: keli19
 ms.reviewer: peterlu
-ms.date: 11/04/2019
+ms.date: 12/25/2019
 ---
 # Train & compare multiple regression models to predict car prices with Azure Machine Learning designer
 
@@ -76,7 +76,7 @@ Here are the results:
 
 These results show that the model built with **Boosted Decision Tree Regression** has a lower root mean squared error than the model built on **Decision Forest Regression**.
 
-Both algorithms have a lower error on the training dataset than on the unseen testing dataset.
+
 
 ## Clean up resources