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, xgboost, sklearn, pandas, matplotlib, numpy, and scipy. pylift is being actively maintained at [github.com/df-foundation/pylift](https://github.com/df-foundation/pylift).

Contents:

• Installation
• Introduction to uplift
  • The Transformed Outcome
  • The Qini curve
  • Alternate Qini-style curves
• Quick start
• Usage: modeling
  • Instantiation
  • Fit and hyperparameter tunings: passing custom parameters
  • Accessing sklearn objects
  • Continuous outcome
• Usage: EDA
  • Net Weight of Evidence
  • Net Information Value
• Usage: evaluation
  • Within the up object
  • With UpliftEval
• Usage: custom targeting policy
• Raw data
  • Evaluation curve information
• Contributing

pylift has two main features:

1. A TransformedOutcome class (inheriting a more general BaseProxyMethod class) that allows for full end-to-end uplift modeling.
2. An UpliftEval class that allows for evaluation of any model prediction. This class is used within the TransformedOutcome class, but can be called independently to evaluate the performance of, for example, scores from a modeling approach external to pylift.

The 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 sklearn.

Indices and tables

• Index
• Module Index
• Search Page