Replace pipeline with experiment

Author: PeterCLu

articles/machine-learning/service/how-to-ui-sample-classification-predict-churn.md

@@ -34,7 +34,7 @@ Here's the completed graph for this experiment:
 
 ## Data
 
-The data for this pipeline is from KDD Cup 2009. It has 50,000 rows and 230 feature columns. The task is to predict churn, appetency, and up-selling for customers who use these features. For more information about the data and the task, see the [KDD website](https://www.kdd.org/kdd-cup/view/kdd-cup-2009).
+The data for this experiment is from KDD Cup 2009. It has 50,000 rows and 230 feature columns. The task is to predict churn, appetency, and up-selling for customers who use these features. For more information about the data and the task, see the [KDD website](https://www.kdd.org/kdd-cup/view/kdd-cup-2009).
 
 ## Experiment summary
 

articles/machine-learning/service/how-to-ui-sample-classification-predict-credit-risk-basic.md

@@ -18,7 +18,7 @@ Learn how to build a machine learning classifier without writing a single line o
 
 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.
 
-Here's the final pipeline graph for this sample:
+Here's the final experiment graph for this sample:
 
 ![Graph of the experiment](media/ui-sample-classification-predict-credit-risk-basic/overall-graph.png)
 
@@ -33,7 +33,7 @@ Here's the final pipeline graph for this sample:
 ## Related sample
 
 [Sample 4 - Classification: Credit Risk Prediction (Cost Sensitive)](how-to-ui-sample-classification-predict-credit-risk-cost-sensitive.md)
-provides an advanced pipeline that solves the same problem as this pipeline. It shows how to perform *cost sensitive* classification by using an **Execute Python Script** module and compare the performance of two binary classification algorithms. Refer to it if you want to learn more about how to build classification pipelines.
+provides an advanced experiment that solves the same problem as this experiment. It shows how to perform *cost sensitive* classification by using an **Execute Python Script** module and compare the performance of two binary classification algorithms. Refer to it if you want to learn more about how to build classification pipelines.
 
 ## Data
 
@@ -42,7 +42,7 @@ The dataset contains 1,000 samples with 20 features and 1 label. Each sample rep
 
 ## Experiment summary
 
-Follow these steps to create the pipeline:
+Follow these steps to create the experiment:
 
 1. Drag the German Credit Card UCI Data dataset module into the experiment's canvas.
 1. Add an **Edit Metadata** module so we can add meaningful names for each column.

articles/machine-learning/service/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive.md

@@ -38,7 +38,7 @@ This sample uses the German Credit Card dataset from the UC Irvine repository. T
 
 ## Experiment summary
 
-In this pipeline, you compare two different approaches for generating models to solve this problem:
+In this experiment, you compare two different approaches for generating models to solve this problem:
 
 - Training with the original dataset.
 - Training with a replicated dataset.
@@ -107,7 +107,7 @@ The following diagram shows a portion of this experiment, in which the original
 
 ![Experiment graph](media/ui-sample-classification-predict-credit-risk-cost-sensitive/score-part.png)
 
-In the evaluation stage of the pipeline, you compute the accuracy of each of the four models. For this pipeline, use **Evaluate Model** to compare examples that have the same misclassification cost.
+In the evaluation stage of the experiment, you compute the accuracy of each of the four models. For this experiment, use **Evaluate Model** to compare examples that have the same misclassification cost.
 
 The **Evaluate Model** module can compute the performance metrics for as many as two scored models. So you can use one instance of **Evaluate Model** to evaluate the two SVM models and another instance of **Evaluate Model** to evaluate the two Boosted Decision Tree models.
 

articles/machine-learning/service/how-to-ui-sample-classification-predict-flight-delay.md

@@ -18,9 +18,9 @@ This experiment uses historical flight and weather data to predict if a schedule
 
 This problem can be approached as a classification problem, predicting two classes -- delayed, or on time. To build a classifier, this model using a large number of examples from historic flight data.
 
-Here's the final pipeline graph for this sample:
+Here's the final experiment graph for this sample:
 
-[![Graph of the pipeline](media/ui-sample-classification-predict-flight-delay/experiment-graph.png)](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
+[![Graph of the experiment](media/ui-sample-classification-predict-flight-delay/experiment-graph.png)](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
 
 ## Prerequisites
 

articles/machine-learning/service/how-to-ui-sample-regression-predict-automobile-price-basic.md

@@ -16,7 +16,7 @@ ms.date: 05/10/2019
 
 Learn how to build a machine learning regression model without writing a single line of code using the visual interface.
 
-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 experiment 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.
 
 The fundamental steps of a training machine learning model are:
 
@@ -39,7 +39,7 @@ Here's the final, completed graph of the experiment we'll be working on. We'll p
 
 ## Get the data
 
-This sample uses the **Automobile price data (Raw)** dataset, which is from the UCI Machine Learning Repository. The dataset contains 26 columns that contain information about automobiles, including make, model, price, vehicle features (like the number of cylinders), MPG, and an insurance risk score. The goal of this pipeline is to predict the price of the car.
+This sample uses the **Automobile price data (Raw)** dataset, which is from the UCI Machine Learning Repository. The dataset contains 26 columns that contain information about automobiles, including make, model, price, vehicle features (like the number of cylinders), MPG, and an insurance risk score. The goal of this experiment is to predict the price of the car.
 
 ## Pre-process the data
 

articles/machine-learning/service/how-to-ui-sample-regression-predict-automobile-price-compare-algorithms.md

@@ -16,7 +16,7 @@ ms.date: 05/10/2019
 
 Learn how to build a complex machine learning experiment without writing a single line of code using the visual interface. This sample trains and compares multiple regression models to predict a car's price based on its technical features. We'll provide the rationale for the choices made in this experiment so you can tackle your own machine learning problems.
 
-If you're just getting started with machine learning, take a look at the [basic version](how-to-ui-sample-regression-predict-automobile-price-basic.md) of this pipeline.
+If you're just getting started with machine learning, take a look at the [basic version](how-to-ui-sample-regression-predict-automobile-price-basic.md) of this experiment.
 
 Here's the completed graph for this experiment:
 
@@ -32,7 +32,7 @@ Here's the completed graph for this experiment:
 
 ## Experiment summary
 
-Use following steps to build the machine learning pipeline:
+Use following steps to build the machine learning experiment:
 
 1. Get the data.
 1. Pre-process the data.
@@ -57,9 +57,9 @@ Machine learning problems vary. Common machine learning tasks include classifica
 
 Because the goal of this experiment is to predict automobile prices, and because the label column (price) contains real numbers, a regression model is a good choice. Considering that the number of features is relatively small (less than 100) and these features aren't sparse, the decision boundary is likely to be nonlinear.
 
-Because the goal of this pipeline is to predict automobile prices, and because the label column (price) contains real numbers, a regression model is a good choice. Considering that the number of features is relatively small (less than 100) and these features aren't sparse, the decision boundary is likely to be nonlinear.
+Because the goal of this experiment is to predict automobile prices, and because the label column (price) contains real numbers, a regression model is a good choice. Considering that the number of features is relatively small (less than 100) and these features aren't sparse, the decision boundary is likely to be nonlinear.
 
-To compare the performance of different algorithms, we use two nonlinear algorithms, **Boosted Decision Tree Regression** and **Decision Forest Regression**, to build models. Both algorithms have parameters that you can change, but this sample uses the default values for this pipeline.
+To compare the performance of different algorithms, we use two nonlinear algorithms, **Boosted Decision Tree Regression** and **Decision Forest Regression**, to build models. Both algorithms have parameters that you can change, but this sample uses the default values for this experiment.
 
 Use the **Split Data** module to randomly divide the input data so that the training dataset contains 70% of the original data and the testing dataset contains 30% of the original data.
 
@@ -69,7 +69,7 @@ You use two different sets of randomly chosen data to train and then test the mo
 
 After the model is trained, use the **Score Model** and **Evaluate Model** modules to generate predicted results and evaluate the models. **Score Model** generates predictions for the test dataset by using the trained model. Then pass the scores to **Evaluate Model** to generate evaluation metrics.
 
-In this pipeline, you use two instances of **Evaluate Model** to compare two pairs of models.
+In this experiment, you use two instances of **Evaluate Model** to compare two pairs of models.
 
 First, compare two algorithms on the training dataset.
 Second, compare two algorithms on the testing dataset.