eli5 0.2

Debug machine learning classifiers and explain their predictions

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

It provides support for the following machine learning frameworks and packages:

• scikit-learn. Currently ELI5 allows to explain weights and predictions of scikit-learn linear classifiers and regressors, print decision trees as text or as SVG, show feature importances of random forests. ELI5 understands text processing utilities from scikit-learn and can highlight text data accordingly. It also allows to debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing.

• lightning - explain weights and predictions of lightning classifiers and regressors.

• sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.

• xgboost - show feature importances using the same interface.

ELI5 also provides an alternative implementation of LIME algorithm, which allows to explain predictions of any black-box classifier. This feature is currently experimental.

Explanation and formatting are separated; you can get text-based explanation to display in console, HTML version embeddable in an IPython notebook or web dashboards, or JSON version which allows to implement custom rendering and formatting on a client.

License is MIT.

Check docs for more.

Changelog

0.2 (2016-12-03)

• XGBClassifier support (from XGBoost package);

• eli5.explain_weights support for sklearn OneVsRestClassifier;

• std deviation of feature importances is no longer printed as zero if it is not available.

0.1.1 (2016-11-25)

• packaging fixes: require attrs > 16.0.0, fixed README rendering

0.1 (2016-11-24)

• HTML output;

• IPython integration;

• JSON output;

• visualization of scikit-learn text vectorizers;

• sklearn-crfsuite support;

• lightning support;

• eli5.show_weights and eli5.show_prediction functions;

• eli5.explain_weights and eli5.explain_prediction functions;

• eli5.lime <eli5-lime> improvements: samplers for non-text data, bug fixes, docs;

• HashingVectorizer is supported for regression tasks;

• performance improvements - feature names are lazy;

• sklearn ElasticNetCV and RidgeCV support;

• it is now possible to customize formatting output - show/hide sections, change layout;

• sklearn OneVsRestClassifier support;

• sklearn DecisionTreeClassifier visualization (text-based or svg-based);

• dropped support for scikit-learn < 0.18;

• basic mypy type annotations;

• feature_re argument allows to show only a subset of features;

• target_names argument allows to change display names of targets/classes;

• targets argument allows to show a subset of targets/classes and change their display order;

• documentation, more examples.

0.0.6 (2016-10-12)

• Candidate features in eli5.sklearn.InvertableHashingVectorizer are ordered by their frequency, first candidate is always positive.

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 (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.

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)

Pre-release.