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ELI5
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ELI5 is a Python package which helps to debug machine learning
classifiers and explain their predictions.
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:alt: explain_prediction for text data
… image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/gradcam-catdog.png
:alt: explain_prediction for image data
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 and explain predictions
of decision trees and tree-based ensembles. ELI5 understands text
processing utilities from scikit-learn and can highlight text data
accordingly. Pipeline and FeatureUnion are supported.
It also allows to debug scikit-learn pipelines which contain
HashingVectorizer, by undoing hashing.
-
Keras_ - explain predictions of image classifiers via Grad-CAM visualizations.
-
xgboost_ - show feature importances and explain predictions of XGBClassifier,
XGBRegressor and xgboost.Booster.
-
LightGBM_ - show feature importances and explain predictions of
LGBMClassifier and LGBMRegressor.
-
CatBoost_ - show feature importances of CatBoostClassifier,
CatBoostRegressor and catboost.CatBoost.
-
lightning_ - explain weights and predictions of lightning classifiers and
regressors.
-
sklearn-crfsuite_. ELI5 allows to check weights of sklearn_crfsuite.CRF
models.
ELI5 also implements several algorithms for inspecting black-box models
(see Inspecting Black-Box Estimators_):
- TextExplainer_ allows to explain predictions
of any text classifier using LIME_ algorithm (Ribeiro et al., 2016).
There are utilities for using LIME with non-text data and arbitrary black-box
classifiers as well, but this feature is currently experimental.
Permutation importance_ method can be used to compute feature importances
for black box estimators.
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, a pandas.DataFrame object if you want to process
results further, or JSON version which allows to implement custom rendering
and formatting on a client.
… _lightning: https://github.com/scikit-learn-contrib/lightning
… _scikit-learn: https://github.com/scikit-learn/scikit-learn
… _sklearn-crfsuite: https://github.com/TeamHG-Memex/sklearn-crfsuite
… _LIME: https://eli5.readthedocs.io/en/latest/blackbox/lime.html
… _TextExplainer: https://eli5.readthedocs.io/en/latest/tutorials/black-box-text-classifiers.html
… _xgboost: https://github.com/dmlc/xgboost
… _LightGBM: https://github.com/Microsoft/LightGBM
… _Catboost: https://github.com/catboost/catboost
… _Keras: https://keras.io/
… _Permutation importance: https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html
… _Inspecting Black-Box Estimators: https://eli5.readthedocs.io/en/latest/blackbox/index.html
License is MIT.
Check docs <https://eli5.readthedocs.io/>_ for more.
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