Welcome to pylift’s documentation!¶
pylift is an uplift library that provides, primarily, (1) fast uplift modeling implementations and (2) evaluation tools. While other packages and more exact methods exist to model uplift, pylift is designed to be quick, flexible, and effective. pylift heavily leverages the optimizations of other packages – namely,
scipy. pylift is being actively maintained at [github.com/df-foundation/pylift](https://github.com/df-foundation/pylift).
- Introduction to uplift
- Quick start
- Usage: modeling
- Usage: EDA
- Usage: evaluation
- Usage: custom targeting policy
- Raw data
pylift has two main features:
TransformedOutcomeclass (inheriting a more general
BaseProxyMethodclass) that allows for full end-to-end uplift modeling.
UpliftEvalclass that allows for evaluation of any model prediction. This class is used within the
TransformedOutcomeclass, but can be called independently to evaluate the performance of, for example, scores from a modeling approach external to pylift.
TransformedOutcome class (and so, the
BaseProxyMethod class) simply wraps
sklearn classes and functions. Therefore, it’s generally possible to do anything you can do with
sklearn within pylift as well. Advanced usage of pylift, therefore, should feel familiar to those well-versed in