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https://github.com/TeamHG-Memex/eli5

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

crfsuite data-science explanation inspection lightgbm machine-learning nlp python scikit-learn xgboost

Last synced: 3 months ago
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A library for debugging/inspecting machine learning classifiers and explaining their predictions

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README 

        
====

ELI5

====



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

   :alt: PyPI Version



.. image:: https://travis-ci.org/TeamHG-Memex/eli5.svg?branch=master

   :target: https://travis-ci.org/TeamHG-Memex/eli5

   :alt: Build Status



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

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   :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/TeamHG-Memex/eli5/master/docs/source/static/word-highlight.png

   :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 `_ for more.



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	:target: https://www.hyperiongray.com/?pk_campaign=github&pk_kwd=eli5

	:alt: define hyperiongray