==== ELI5

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

.. image:: https://raw.githubusercontent.com/eli5-org/eli5/refs/heads/master/docs/source/static/readme-show-prediction.png :alt: explain_prediction for text data

.. image:: https://raw.githubusercontent.com/eli5-org/eli5/refs/heads/master/docs/source/static/gradcam-catdog.png :alt: explain_prediction for image data

.. image:: https://raw.githubusercontent.com/eli5-org/eli5/refs/heads/master/docs/source/static/readme-show-weights.png :alt: explain_weights for text 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, LGBMRegressor and lightgbm.Booster.

• 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.

• OpenAI_ python client. ELI5 allows to explain LLM predictions with token probabilities.

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/scrapinghub/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 .. _OpenAI: https://github.com/openai/openai-python

License is MIT.

Check docs <https://eli5.readthedocs.io/>_ for more.

.. note:: This project was previously developed at https://github.com/TeamHG-Memex/eli5/ with support from Hyperion Gray <https://www.hyperiongray.com>_.