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Debug machine learning classifiers and explain their predictions

Project description

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ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions.

Currently it allows to:

  • explain weights and predictions of scikit-learn linear classifiers and regressors;

  • explain weights of scikit-learn decision trees and tree-based ensemble classifiers (via feature importances);

  • debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing;

  • explain predictions of any black-box classifier using LIME ( ) algorithm.


License is MIT.

Check docs for more (sorry, also TODO).


0.0.5 (2016-09-27)

  • HashingVectorizer support in explain_prediction;

  • add an option to pass coefficient scaling array; it is useful if you want to compare coefficients for features which scale or sign is different in the input;

  • bug fix: classifier weights are no longer changed by eli5 functions.

0.0.4 (2016-09-24)

  • eli5.sklearn.InvertableHashingVectorizer and eli5.sklearn.FeatureUnhasher allow to recover feature names for pipelines which use HashingVectorizer or FeatureHasher;

  • added support for scikit-learn linear regression models (ElasticNet, Lars, Lasso, LinearRegression, LinearSVR, Ridge, SGDRegressor);

  • doc and vec arguments are swapped in explain_prediction function; vec can now be omitted if an example is already vectorized;

  • fixed issue with dense feature vectors;

  • all class_names arguments are renamed to target_names;

  • feature name guessing is fixed for scikit-learn ensemble estimators;

  • testing improvements.

0.0.3 (2016-09-21)

  • support any black-box classifier using LIME ( algorithm; text data support is built-in;

  • “vectorized” argument for sklearn.explain_prediction; it allows to pass example which is already vectorized;

  • allow to pass feature_names explicitly;

  • support classifiers without get_feature_names method using auto-generated feature names.

0.0.2 (2016-09-19)

  • ‘top’ argument of explain_prediction can be a tuple (num_positive, num_negative);

  • classifier name is no longer printed by default;

  • added eli5.sklearn.explain_prediction to explain individual examples;

  • fixed numpy warning.

0.0.1 (2016-09-15)


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