Confidence Intervals for Scikit Learn Random Forests

Random forest algorithms are useful for both classification and regression problems. This package adds to scikit-learn the ability to calculate confidence intervals of the predictions generated from scikit-learn sklearn.ensemble.RandomForestRegressor and sklearn.ensemble.RandomForestClassifier objects.

This is an implementation of an algorithm developed by Wager et al. [Wager2014] and previously implemented in R (here).

To examine and download the source code, visit our github repo.

Wager2014

19. Wager, T. Hastie, B. Efron. “Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife”, Journal of Machine Learning Research vol. 15, pp. 1625-1651, 2014.

• Installation Guide
• forestci API
  • forestci.random_forest_error
• Examples
• Contributing to forestci development
  • Code of Conduct

Acknowledgements: this work was supported by a grant from the Gordon & Betty Moore Foundation, and from the Alfred P. Sloan Foundation to the University of Washington eScience Institute , and through a grant from the Bill & Melinda Gates Foundation.