Debug machine learning classifiers and explain their predictions
Project description
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions.
It can explain weights and predictions of:
scikit-learn linear classifiers;
scikit-learn decision trees and tree-based ensemble classifiers;
any black-box classifier using LIME ( http://arxiv.org/abs/1602.04938 ) algorithm.
TODO:
https://github.com/TeamHG-Memex/sklearn-crfsuite and https://github.com/tpeng/python-crfsuite
fasttext (?)
xgboost (?)
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)
eli5.lime improvements;
IPython and HTML support;
regression models;
Reinforcement Learning support.
License is MIT.
Check docs for more (sorry, also TODO).
Changelog
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.
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