Skip to main content

Permutation and drop-column importance for scikit-learn random forests and other models

Project description

A library that provides feature importances, based upon the permutation importance strategy, for general scikit-learn models and implementations specifically for random forest out-of-bag scores. Built by Terence Parr and Kerem Turgutlu. See <a href=””>Beware Default Random Forest Importances</a> for a deeper discussion of the issues surrounding feature importances in random forests.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
rfpimp-1.3.tar.gz (11.1 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page