Concept

# Lift (data mining)

Summary
In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target (T) is much better than the baseline (B) average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. Mathematically, : \operatorname{lift} = \frac{P(T \mid B)}{P(T)} = \frac{P(T\wedge B)}{P(T)P(B)} For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%). Applications Typically, the modeller seeks to divide the population into quantiles, and rank the quantiles by lift.
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