Lift (Data Mining)
Swipe Lift (Data Mining) Vs...
Lift (Data Mining) News
Description
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 {\displaystyle T} ) is much better than the baseline ( B {\displaystyle B} ) average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. Mathematically,
lift = P ( T ∣ B ) P ( T ) = P ( T ∧ B ) P ( T ) P ( B ) {\displaystyle \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%).
Related
More
Collections
Details
- Slug: lift-data-mining
- Added: Jul 20, 2024