Compute the statistical impact of features given a trained estimator
Project description
# featureimpact
[](https://github.com/bloomen/featureimpact/actions/workflows/featureimpact-tests.yml?query=branch%3Amaster)
featureimpact let’s you compute the statistical impact of features given a trained estimator. The computation is based on the mean variation of the difference between perturbed and original predictions. The estimator must predict purely numerical values. All features must also consist of purely numerical values.
Example: `python from featureimpact import FeatureImpact fi = FeatureImpact() fi.make_quantiles(X_train) impact = fi.compute_impact(model, X_test) `
Note: In order to run the examples you’ll need scikit-learn installed in addition to this package and its regular dependencies.
The algorithm is described here: https://bloomen.github.io/pub/featureimpact.pdf
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
](https://github.com/bloomen/featureimpact/actions/workflows/featureimpact-tests.yml){kind=link}