Compute the statistical impact of features given a trained estimator
Project description
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: ` 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 impact estimation of this package follows the approach in Section 3.9.2 of ` Blume, C., 2012: Statistical Learning To Model Stratospheric Variability. Doctoral thesis, Institute for Meteorology, Freie Universität Berlin. https://refubium.fu-berlin.de/handle/fub188/13901 ` and extends it to more than one quantile.
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