Lift

Lift measures the degree to which the predictions of a Classification model are better than randomly-generated predictions. Lift applies to binary classification and non-binary classifications.

Lift measures how rapidly the model finds the actual positive target values. For example, lift enables you to figure how much of the customer database you must contact to get 50 percent of the customers likely to respond to an offer.

The x-axis of the graph is divided into quantiles. To view exact values, place the cursor over the graph. Below the graph, you can select the quantile of interest using Selected Quantile. The default quantile is quantile 1.

To calculate lift, Oracle Data Mining does the following:

• Applies the model to test data to gather predicted and actual target values. This is the same data used to calculate the Performance Matrix.

• Sorts the predicted results by probability, that is, the confidence in a positive prediction.

• Divides the ranked list into equal parts, quantiles. The default is 100.

• Counts the actual positive values in each quantile.

You can graph the lift as either Cumulative Lift or as Cumulative Positive Cases (default). To change the graph, select the appropriate value from the Display list. You can also select a target value in the Target Value list.