Metadata-Version: 1.1
Name: featureimpact
Version: 2.1.0
Summary: Compute the statistical impact of features given a trained estimator
Home-page: https://github.com/bloomen/featureimpact
Author: Christian Blume
Author-email: chr.blume@gmail.com
License: MIT
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.
        
Keywords: machine learning,data mining,statistics
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires: numpy
Requires: scipy
Requires: pandas
