Rename media file name references

Author: PeterCLu

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

@@ -22,15 +22,15 @@ Because you're trying to answer the question "Which one?" this is called a class
 
 Here's the completed graph for this experiment:
 
-![Experiment graph](./media/ui-sample-classification-predict-churn/experiment-graph.png)
+![Experiment graph](./media/how-to-ui-sample-classification-predict-churn/experiment-graph.png)
 
 ## Prerequisites
 
 [!INCLUDE [aml-ui-prereq](../../../includes/aml-ui-prereq.md)]
 
 4. Select the **Open** button for the Sample 5 experiment.
 
-    ![Open the experiment](media/ui-sample-classification-predict-churn/open-sample5.png)
+    ![Open the experiment](media/how-to-ui-sample-classification-predict-churn/open-sample5.png)
 
 ## Data
 
@@ -44,11 +44,11 @@ First, do some simple data processing.
 
 - The raw dataset contains lots of missing values. Use the **Clean Missing Data** module to replace the missing values with 0.
 
-    ![Clean the dataset](./media/ui-sample-classification-predict-churn/cleaned-dataset.png)
+    ![Clean the dataset](./media/how-to-ui-sample-classification-predict-churn/cleaned-dataset.png)
 
 - The features and the corresponding churn, appetency, and up-selling labels 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. The rest of the columns, **Var1**, **Var2**, and so on, are the feature columns.
 
-    ![Add the column dataset](./media/ui-sample-classification-predict-churn/added-column1.png)
+    ![Add the column dataset](./media/how-to-ui-sample-classification-predict-churn/added-column1.png)
 
 - Use the **Split Data** module to split the dataset into train and test sets.
 
@@ -58,7 +58,7 @@ First, do some simple data processing.
 
 Visualize the output of the **Evaluate Model** module to see the performance of the model on the test set. For the up-selling task, the ROC curve shows that the model does better than a random model. The area under the curve (AUC) is 0.857. At threshold 0.5, the precision is 0.7, the recall is 0.463, and the F1 score is 0.545.
 
-![Evaluate the results](./media/ui-sample-classification-predict-churn/evaluate-result.png)
+![Evaluate the results](./media/how-to-ui-sample-classification-predict-churn/evaluate-result.png)
 
  You can move the **Threshold** slider and see the metrics change for the binary classification task.
 

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

@@ -20,15 +20,15 @@ Because the question is answering "Which one?" this is called a classification p
 
 Here's the final experiment graph for this sample:
 
-![Graph of the experiment](media/ui-sample-classification-predict-credit-risk-basic/overall-graph.png)
+![Graph of the experiment](media/how-to-ui-sample-classification-predict-credit-risk-basic/overall-graph.png)
 
 ## Prerequisites
 
 [!INCLUDE [aml-ui-prereq](../../../includes/aml-ui-prereq.md)]
 
 4. Select the **Open** button for the Sample 3 experiment:
 
-    ![Open the experiment](media/ui-sample-classification-predict-credit-risk-basic/open-sample3.png)
+    ![Open the experiment](media/how-to-ui-sample-classification-predict-credit-risk-basic/open-sample3.png)
 
 ## Related sample
 
@@ -54,7 +54,7 @@ Follow these steps to create the experiment:
 
 ## Results
 
-![Evaluate the results](media/ui-sample-classification-predict-credit-risk-basic/evaluate-result.png)
+![Evaluate the results](media/how-to-ui-sample-classification-predict-credit-risk-basic/evaluate-result.png)
 
 In the evaluation results, you can see that the AUC of the model is 0.776. At threshold 0.5, the precision is 0.621, the recall is 0.456, and the F1 score is 0.526.
 

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

@@ -22,15 +22,15 @@ If you're just getting started with machine learning, you can take a look at the
 
 Here's the completed graph for this experiment:
 
-[![Graph of the experiment](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png)](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
+[![Graph of the experiment](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png)](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
 
 ## Prerequisites
 
 [!INCLUDE [aml-ui-prereq](../../../includes/aml-ui-prereq.md)]
 
 4. Select the **Open** button for the Sample 4 experiment:
 
-    ![Open the experiment](media/ui-sample-classification-predict-credit-risk-cost-sensitive/open-sample4.png)
+    ![Open the experiment](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/open-sample4.png)
 
 ## Data
 
@@ -49,7 +49,7 @@ The cost of misclassifying a low-risk example as high is 1, and the cost of misc
 
 Here's the graph of the experiment:
 
-[![Graph of the experiment](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png)](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
+[![Graph of the experiment](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png)](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
 
 ## Data processing
 
@@ -105,7 +105,7 @@ This sample uses the standard data science workflow to create, train, and test t
 
 The following diagram shows a portion of this experiment, in which the original and replicated training sets are used to train two different SVM models. **Train Model** is connected to the training set, and **Score Model** is connected to the test set.
 
-![Experiment graph](media/ui-sample-classification-predict-credit-risk-cost-sensitive/score-part.png)
+![Experiment graph](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/score-part.png)
 
 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.
 
@@ -139,7 +139,7 @@ def azureml_main(dataframe1 = None, dataframe2 = None):
 
 To view the results of the experiment, you can right-click the Visualize output of the last **Select Columns in Dataset** module.
 
-![Visualize output](media/ui-sample-classification-predict-credit-risk-cost-sensitive/result.png)
+![Visualize output](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/result.png)
 
 The first column lists the machine learning algorithm used to generate the model.
 The second column indicates the type of the training set.

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

@@ -20,15 +20,15 @@ This problem can be approached as a classification problem, predicting two class
 
 Here's the final experiment graph for this sample:
 
-[![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)
+[![Graph of the experiment](media/how-to-ui-sample-classification-predict-flight-delay/experiment-graph.png)](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
 
 ## Prerequisites
 
 [!INCLUDE [aml-ui-prereq](../../../includes/aml-ui-prereq.md)]
 
 4. Select the **Open** button for the Sample 6 experiment:
 
-    ![Open the experiment](media/ui-sample-classification-predict-flight-delay/open-sample6.png)
+    ![Open the experiment](media/how-to-ui-sample-classification-predict-flight-delay/open-sample6.png)
 
 ## Get the data
 
@@ -50,13 +50,13 @@ To supplement the flight data, the **Weather Dataset** is used. The weather data
 
 A dataset usually requires some pre-processing before it can be analyzed.
 
-![data-process](media/ui-sample-classification-predict-flight-delay/data-process.png)
+![data-process](media/how-to-ui-sample-classification-predict-flight-delay/data-process.png)
 
 ### Flight data
 
 The columns **Carrier**, **OriginAirportID**, and **DestAirportID** are saved as integers. However, they're  categorical attributes, use the **Edit Metadata** module to convert them to categorical.
 
-![edit-metadata](media/ui-sample-classification-predict-flight-delay/edit-metadata.png)
+![edit-metadata](media/how-to-ui-sample-classification-predict-flight-delay/edit-metadata.png)
 
 Then use the **Select Columns** in Dataset module to exclude from the dataset columns that are possible target leakers: **DepDelay**, **DepDel15**, **ArrDelay**, **Canceled**, **Year**. 
 
@@ -76,18 +76,18 @@ Since weather data is reported in local time, time zone differences are accounte
 
 Flight records are joined with weather data at origin of the flight (**OriginAirportID**) using the **Join Data** module.
 
- ![join flight and weather by origin](media/ui-sample-classification-predict-flight-delay/join-origin.png)
+ ![join flight and weather by origin](media/how-to-ui-sample-classification-predict-flight-delay/join-origin.png)
 
 
 Flight records are joined with weather data using the destination of the flight (**DestAirportID**).
 
- ![Join flight and weather by destination](media/ui-sample-classification-predict-flight-delay/join-destination.png)
+ ![Join flight and weather by destination](media/how-to-ui-sample-classification-predict-flight-delay/join-destination.png)
 
 ### Preparing Training and Test Samples
 
 The **Split Data** module splits the data into April through September records for training, and October records for test.
 
- ![Split training and test data](media/ui-sample-classification-predict-flight-delay/split.png)
+ ![Split training and test data](media/how-to-ui-sample-classification-predict-flight-delay/split.png)
 
 Year, month, and timezone columns are removed from the training dataset using the Select Columns module.
 
@@ -110,7 +110,7 @@ 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/ui-sample-classification-predict-flight-delay/evaluate.png)
+ ![evaluate](media/how-to-ui-sample-classification-predict-flight-delay/evaluate.png)
 
 ## Next steps
 

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

@@ -27,15 +27,15 @@ The fundamental steps of a training machine learning model are:
 
 Here's the final, completed graph of the experiment we'll be working on. We'll provide the rationale for all the modules so you can make similar decisions on your own.
 
-![Graph of the experiment](media/ui-sample-regression-predict-automobile-price-basic/overall-graph.png)
+![Graph of the experiment](media/how-to-ui-sample-regression-predict-automobile-price-basic/overall-graph.png)
 
 ## Prerequisites
 
 [!INCLUDE [aml-ui-prereq](../../../includes/aml-ui-prereq.md)]
 
 4. Select the **Open** button for the Sample 1 experiment:
 
-    ![Open the experiment](media/ui-sample-regression-predict-automobile-price-basic/open-sample1.png)
+    ![Open the experiment](media/how-to-ui-sample-regression-predict-automobile-price-basic/open-sample1.png)
 
 ## Get the data
 
@@ -47,7 +47,7 @@ The main data preparation tasks include data cleaning, integration, transformati
 
 Use the **Select Columns in Dataset** module to exclude normalized-losses that have many missing values. Then use **Clean Missing Data** to remove the rows that have missing values. This helps to create a clean set of training data.
 
-![Data pre-processing](./media/ui-sample-regression-predict-automobile-price-basic/data-processing.png)
+![Data pre-processing](./media/how-to-ui-sample-regression-predict-automobile-price-basic/data-processing.png)
 
 ## Train the model
 
@@ -65,11 +65,11 @@ 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/ui-sample-regression-predict-automobile-price-basic/score-result.png)
+![Score result](./media/how-to-ui-sample-regression-predict-automobile-price-basic/score-result.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/ui-sample-regression-predict-automobile-price-basic/evaluate-result.png)
+![Evaluate result](./media/how-to-ui-sample-regression-predict-automobile-price-basic/evaluate-result.png)
 
 ## Clean up resources
 

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

@@ -20,15 +20,15 @@ If you're just getting started with machine learning, take a look at the [basic
 
 Here's the completed graph for this experiment:
 
-[![Graph of the experiment](media/ui-sample-regression-predict-automobile-price-compare-algorithms/graph.png)](media/ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
+[![Graph of the experiment](media/how-to-ui-sample-regression-predict-automobile-price-compare-algorithms/graph.png)](media/how-to-ui-sample-classification-predict-credit-risk-cost-sensitive/graph.png#lightbox)
 
 ## Prerequisites
 
 [!INCLUDE [aml-ui-prereq](../../../includes/aml-ui-prereq.md)]
 
 4. Select the **Open** button for the Sample 2 experiment:
 
-    ![Open the experiment](media/ui-sample-regression-predict-automobile-price-compare-algorithms/open-sample2.png)
+    ![Open the experiment](media/how-to-ui-sample-regression-predict-automobile-price-compare-algorithms/open-sample2.png)
 
 ## Experiment summary
 
@@ -49,7 +49,7 @@ The main data preparation tasks include data cleaning, integration, transformati
 
 Use the **Select Columns in Dataset** module to exclude normalized-losses that have many missing values. We then use **Clean Missing Data** to remove the rows that have missing values. This helps to create a clean set of training data.
 
-![Data pre-processing](media/ui-sample-regression-predict-automobile-price-compare-algorithms/data-processing.png)
+![Data pre-processing](media/how-to-ui-sample-regression-predict-automobile-price-compare-algorithms/data-processing.png)
 
 ## Train the model
 
@@ -76,7 +76,7 @@ Second, compare two algorithms on the testing dataset.
 
 Here are the results:
 
-![Compare the results](media/ui-sample-regression-predict-automobile-price-compare-algorithms/result.png)
+![Compare the results](media/how-to-ui-sample-regression-predict-automobile-price-compare-algorithms/result.png)
 
 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**.