Lift

Lift measures the degree to which the predictions of a Classification model are better than randomly generated predictions.

To tune a model using Lift:

1. Open the Properties pane for the Build node. Right-click the node and select Go to Properties.

2. In the Test section, select Generate Select Test Results for Model Tuning and run the node.

3. In the Models section, select the models that you want to tune and click .

4. Select Tune from the drop-down list. The Tune Settings dialog box opens in a new tab.

5. In Tune Settings dialog box, go to the Lift tab.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane. After you tune the first model, return to this pane and select another model.

7. Select the target value for tuning from the Target Value list.

8. Decide whether to tune using the Cumulative Positive Cases chart, the default or the Cumulative Lift Chart. Select the chart from the Display list.

   Either chart displays several curves: the lift curve for the model that you are tuning, ideal lift, and random lift, which is the lift from a model where predictions are random.

   The chart also displays a blue vertical line that indicates the threshold, the quantile of interest.

9. Selected a quantile using the slider in the quantile display below the lift chart. As you move the slider, the blue vertical bar moves to that quantile, and the tuning panel is updated with the Performance Matrix for that point.

10. Click Tune, below the Performance Matrix. New tune settings are displayed in the same panel as the Performance Matrix. Examine the Derived Cost Matrix. You can continue tuning by changing any selections that you made.

11. When you have finished, click OK to accept the tuning, or click Cancel to cancel the tuning.

    • To reset the tuning, click Reset.

    • To see the impact of the tuning, run the Model node.