After you've used data transformations to prepare the data, you can use it to train a machine learning model.

Add training modules

It's common practice to train the model using a subset of the data, while holding back some data with which to test the trained model. This enables you to compare the labels that the model predicts with the actual known labels in the original dataset.

In this exercise, you're going to extend the Auto Price Training pipeline as shown here:

Follow the steps below, using the image above for reference as you add and configure the required modules.

1. Open the Auto Price Training pipeline you created in the previous unit if it's not already open.

2. In the pane on the left, in the Data Transformations section, drag a Split Data module onto the canvas under the Normalize Data module. Then connect the Transformed Dataset (left) output of the Normalize Data module to the input of the Split Data module.

3. Select the Split Data module, and configure its settings as follows:

• Splitting mode : Split Rows

• Fraction of rows in the first output dataset : 0.7

• Random seed : 123

• Stratified split : False

4. Expand the Model Training section in the pane on the left, and drag a Train Model module to the canvas, under the Split Data module. Then connect the Result dataset1 (left) output of the Split Data module to the Dataset (right) input of the Train Model module.

5. The model we're training will predict the price value, so select the Train Model module and modify its settings to set the Label column to price (matching the case and spelling exactly!)

6. The price label the model will predict is a numeric value, so we need to train the model using a regression algorithm. Expand the Machine Learning Algorithms section, and under Regression , drag a Linear Regression module to the canvas, to the left of the Split Data module and above the Train Model module. Then connect its output to the Untrained model (left) input of the Train Model module.

Note

There are multiple algorithms you can use to train a regression model. For help choosing one, take a look at the  https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet for Microsoft Azure Machine Learning designer.

7. To test the trained model, we need to use it to score the validation dataset we held back when we split the original data - in other words, predict labels for the features in the validation dataset. Expand the Model Scoring & Evaluation section and drag a Score Model module to the canvas, below the Train Model module. Then connect the output of the Train Model module to the Trained model (left) input of the Score Model module; and drag the Results dataset2 (right) output of the Split Data module to the Dataset (right) input of the Score Model module.

8. Ensure your pipeline looks like this:

Run the training pipeline

Now you're ready to run the training pipeline and train the model.

1. Select Submit , and run the pipeline using the existing experiment named mslearn-auto-training .

2. Wait for the experiment run to complete. This may take 5 minutes or more.

3. When the experiment run has completed, select the Score Model module and in the settings pane, on the Outputs + logs tab, under Data outputs in the Scored dataset section, use the Visualize icon to view the results.

4. Scroll to the right, and note that next to the price column (which contains the known true values of the label) there is a new column named Scored labels , which contains the predicted label values.

5. Close the Score Model result visualization window.

The model is predicting values for the price label, but how reliable are its predictions? To assess that, you need to evaluate the model.