featureimpact

Name	featureimpact JSON
Version	2.2.2 JSON
	download
home_page	https://github.com/bloomen/featureimpact
Summary	Compute the statistical impact of features given a trained estimator
upload_time	2023-01-17 05:10:02
maintainer
docs_url	None
author	Christian Blume
requires_python
license	MIT
keywords	machine learning data mining statistics
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # featureimpact

[![Actions](https://github.com/bloomen/featureimpact/actions/workflows/featureimpact-tests.yml/badge.svg?branch=master)](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
            

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