Merge pull request #96503 from PeterCLu/plu-maintenance

Doc maintenance

Author: PRMerger18

articles/machine-learning/algorithm-module-reference/assign-data-to-clusters.md

@@ -9,15 +9,14 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: zhanxia
-ms.date: 05/06/2019
+ms.date: 11/19/2019
 ---
 # Module: Assign Data to Clusters
 
 This article describes how to use the *Assign Data to Clusters* module in Azure Machine Learning designer (preview). The module generates predictions through a clustering model that was trained with the *K-means clustering* algorithm.
 
 The Assign Data to Clusters module returns a dataset that contains the probable assignments for each new data point. 
 
-
 ## How to use Assign Data to Clusters
 
 1. In Azure Machine Learning designer, locate a previously trained clustering model. You can create and train a clustering model by using either of the following methods:  

articles/machine-learning/algorithm-module-reference/create-python-model.md

@@ -9,13 +9,13 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: zhanxia
-ms.date: 05/06/2019
+ms.date: 11/19/2019
 ---
 # Create Python Model
 
-This article describes how to use the **Create Python Model** module to create an untrained model from a Python script. 
+This article describes a module in Azure Machine Learning designer (preview).
 
-You can base the model on any learner that is included in an Python package in the Azure Machine Learning designer environment. 
+Learn how to use the **Create Python Model** module to create an untrained model from a Python script. You can base the model on any learner that is included in a Python package in the Azure Machine Learning designer environment. 
 
 After you create the model, you can use [Train Model](train-model.md) to train the model on a dataset, like any other learner in Azure Machine Learning. The trained model can be passed to [Score Model](score-model.md) to use the model to make predictions. The trained model can then be saved, and the scoring workflow can be published as a web service.
 

articles/machine-learning/algorithm-module-reference/evaluate-model.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: zhanxia
-ms.date: 05/06/2019
+ms.date: 11/19/2019
 ---
 # Evaluate Model module
 
@@ -23,7 +23,6 @@ Use this module to measure the accuracy of a trained model. You provide a datase
 -   **Regression Models**    
 
 
-
 > [!TIP]
 > If you are new to model evaluation, we recommend the video series by Dr. Stephen Elston, as part of the [machine learning course](https://blogs.technet.microsoft.com/machinelearning/2015/09/08/new-edx-course-data-science-machine-learning-essentials/) from EdX. 
 
@@ -102,7 +101,7 @@ The following metrics are reported when evaluating classification models. If you
 
 ##  <a name="bkmk_regression"></a> Metrics for regression models
 
-The metrics returned for regression models are generally designed to estimate the amount of error.  A model is considered to fit the data well if the difference between observed and predicted values is small. However, looking at the pattern of the residuals (the difference between any one predicted point and its corresponding actual value) can tell you a lot about potential bias in the model.  
+The metrics returned for regression models are designed to estimate the amount of error.  A model is considered to fit the data well if the difference between observed and predicted values is small. However, looking at the pattern of the residuals (the difference between any one predicted point and its corresponding actual value) can tell you a lot about potential bias in the model.  
 
  The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.  
 

articles/machine-learning/algorithm-module-reference/execute-r-script.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: peterlu
-ms.date: 06/01/2019
+ms.date: 11/19/2019
 ---
 
 # Execute R Script

articles/machine-learning/algorithm-module-reference/join-data.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: peterlu
-ms.date: 06/01/2019
+ms.date: 11/19/2019
 ---
 
 # Join Data

articles/machine-learning/algorithm-module-reference/k-means-clustering.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: zhanxia
-ms.date: 05/06/2019
+ms.date: 11/19/2019
 ---
 # Module: K-Means Clustering
 
@@ -136,4 +136,4 @@ You can avoid this problem in a couple of ways:
 
 -   Create multiple models, varying the metric or iterating more.  
 
-In general, with clustering models, it's possible that any given configuration will result in a locally optimized set of clusters. In other words, the set of clusters that's returned by the model suits only the current data points and isn't generalizable to other data. If you use a different initial configuration, the K-means method might find a different, perhaps superior, configuration. 
+In general, with clustering models, it's possible that any given configuration will result in a locally optimized set of clusters. In other words, the set of clusters that's returned by the model suits only the current data points and isn't generalizable to other data. If you use a different initial configuration, the K-means method might find a different, superior, configuration. 

articles/machine-learning/algorithm-module-reference/multiclass-boosted-decision-tree.md

@@ -9,8 +9,7 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: zhanxia
-ms.date: 08/22/2019
-ROBOTS: NOINDEX
+ms.date: 11/19/2019
 ---
 
 # Multiclass Boosted Decision Tree

articles/machine-learning/algorithm-module-reference/train-clustering-model.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: xiaoharper
 ms.author: zhanxia
-ms.date: 05/06/2019
+ms.date: 11/19/2019
 ---
 # Train Clustering Model
 
@@ -47,12 +47,10 @@ After training has completed:
 
 +  To view the values in the dataset, right-click the module, select **Result datasets**, and click **Visualize**.
 
-+ To save the trained model for later re-use, right-click the module, select **Trained model**, and click **Save As Trained Model**.
++ To save the trained model for later reuse, right-click the module, select **Trained model**, and click **Save As Trained Model**.
 
 + To generate scores from the model, use [Assign Data to Clusters](assign-data-to-clusters.md).
 
-
-
 ## Next steps
 
 See the [set of modules available](module-reference.md) to Azure Machine Learning.