Lift

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

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

To calculate lift, Oracle Data Mining applies the model to test data to gather predicted and actual target values (the same data that is used to calculate the Confusion matrix), sorts the predicted results by probability (that is, Confidence in a positive prediction), divides the ranked list into equal parts (quantiles - the default number is 10), and then counts the Actual positive values in each quantile.

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

The x-axis of the graph is divided into quantiles. To view exact values, float the cursor over the graph.

Below the graph, you can select the quantile of interest using Selected Quantile. The default quantile is quantile 1.