https://github.com/eli5-org/eli5

A library for debugging/inspecting machine learning classifiers and explaining their predictions
https://github.com/eli5-org/eli5

Last synced: about 1 month ago
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A library for debugging/inspecting machine learning classifiers and explaining their predictions

• Host: GitHub
• URL: https://github.com/eli5-org/eli5
• Owner: eli5-org
• License: mit
• Created: 2021-01-20T08:02:26.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2025-04-20T16:38:15.000Z (7 months ago)
• Last Synced: 2025-10-10T22:59:40.539Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 68.5 MB
• Stars: 312
• Watchers: 3
• Forks: 45
• Open Issues: 12
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGES.rst
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-mlops - ELI5 - Python package which helps to debug machine learning classifiers and explain their predictions. (Model Interpretability)

README

          
====

ELI5

====

.. image:: https://img.shields.io/pypi/v/eli5.svg

   :target: https://pypi.python.org/pypi/eli5

   :alt: PyPI Version

.. image:: https://github.com/eli5-org/eli5/actions/workflows/python-package.yml/badge.svg?branch=master

   :target: https://github.com/eli5-org/eli5/actions

   :alt: Build Status

.. image:: https://codecov.io/github/TeamHG-Memex/eli5/coverage.svg?branch=master

   :target: https://codecov.io/github/TeamHG-Memex/eli5?branch=master

   :alt: Code Coverage

.. image:: https://readthedocs.org/projects/eli5/badge/?version=latest

   :target: https://eli5.readthedocs.io/en/latest/?badge=latest

   :alt: Documentation

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 `_ for more.

.. note::

    This project was previously developed at https://github.com/TeamHG-Memex/eli5/

    with support from `Hyperion Gray `_.