Workflow: Predicting a binary variable with the Decision Forest block

IanBD

The Decision Forest block enables you to apply a decision forest predictive model to an input dataset.

The following demonstrates how to use the Decision Forest block to model the Default variable from the loan_data.csv dataset (containing observations each of which describes a completed loan and the person who took the loan out) based on other independent variables:

1. Import the loan_data.csv dataset onto a Workflow canvas using the Text File Import block.
2. Expand the Model Training group in the Workflow palette, then click and drag a Decision Forest block onto the Workflow canvas.
3. Click the Output port of the loan_data dataset block and drag a connection towards the Input port of the Decision Forest block.
4. Double-click the Decision Forest block to display the Configure Decision Forest dialog box.
5. In the Configure Decision Forest dialog box:
   1. In the Dependent variable drop-down list, select Default.
   2. In the Dependent variable treatment drop-down list, select Nominal.
   3. In the Unselected Independent Variables list, press and hold CTRL and select Age, Income, Loan_Period, Other_Debt, and Part_Of_UK.
   4. Click Select to move the specified to the Selected Independent Variables list.
   
6. Click OK to save the configuration and close the Configure Decision Forest dialog box.

A green execution status is displayed in the Output ports of the Decision Forest block and the new Decision Forest Model. The Decision Forest block output can be used with a Score block to make predictions on a dataset.