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Lift is the ratio of positive responders in a segment to the positive responders in the population as a whole. For example, if a population has a predicted response rate of 20 percent, but one segment of the population has a predicted response rate of 60 percent, then the lift of that segment is 3 (60 percent/20 percent). Lift measures the following:
The concentration of positive predictions within segments of the population and specifies the improvement over the rate of positive predictions in the population as a whole.
The performance of targeting models in marketing applications. The purpose of a targeting model is to identify segments of the population with potentially high concentrations of positive responders to a marketing campaign.
The notion of lift implies a binary target: either a Responder or a Non-responder, which means either YES or NO. Lift can be computed for multiclass targets by designating a preferred positive class and combining all other target class values, effectively turning a multiclass target into a binary target. Lift can be applied to both binary and non-binary classifications as well.
The calculation of lift begins by applying the model to test data in which the target values are already known. Then, the predicted results are sorted in order of probability, from highest to lowest Predictive Confidence. The ranked list is divided into quantiles (equal parts). The default number of quantiles is 100.