Debug machine learning classifiers and explain their predictions
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 ( http://arxiv.org/abs/1602.04938 ) algorithm.
- IPython and HTML support
- https://github.com/TeamHG-Memex/sklearn-crfsuite and https://github.com/tpeng/python-crfsuite
- fasttext (?)
- xgboost (?)
- eli5.lime improvements
- image input
- built-in support for non-text data in eli5.lime
- tensorflow, theano, lasagne, keras
- Naive Bayes from scikit-learn (see https://github.com/scikit-learn/scikit-learn/issues/2237)
- Reinforcement Learning support
- explain predictions of decision trees and treee-based ensembles
License is MIT.
Check docs for more (sorry, also TODO).
- 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.
- support any black-box classifier using LIME (http://arxiv.org/abs/1602.04938) 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.
- ‘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.
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