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AI Safety, Alignment \u0026 Interpretability","Uncategorized","📚 Project Purpose"],"sub_categories":["NLP","Others","Evaluation methods","Interpretability/Explicability","Explaining Predictions","Interpretability \u0026 Explainability","Subtask Scheduling with Human Annotation","Open Source/Access Responsible AI Software Packages","Uncategorized","Machine Learning (Intermediate-Level"],"readme":"====\nELI5\n====\n\n.. image:: https://img.shields.io/pypi/v/eli5.svg\n   :target: https://pypi.python.org/pypi/eli5\n   :alt: PyPI Version\n\n.. image:: https://travis-ci.org/TeamHG-Memex/eli5.svg?branch=master\n   :target: https://travis-ci.org/TeamHG-Memex/eli5\n   :alt: Build Status\n\n.. image:: https://codecov.io/github/TeamHG-Memex/eli5/coverage.svg?branch=master\n   :target: https://codecov.io/github/TeamHG-Memex/eli5?branch=master\n   :alt: Code Coverage\n\n.. image:: https://readthedocs.org/projects/eli5/badge/?version=latest\n   :target: https://eli5.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation\n\n\nELI5 is a Python package which helps to debug machine learning\nclassifiers and explain their predictions.\n\n.. image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/word-highlight.png\n   :alt: explain_prediction for text data\n\n.. image:: https://raw.githubusercontent.com/TeamHG-Memex/eli5/master/docs/source/static/gradcam-catdog.png\n   :alt: explain_prediction for image data\n\nIt provides support for the following machine learning frameworks and packages:\n\n* scikit-learn_. Currently ELI5 allows to explain weights and predictions\n  of scikit-learn linear classifiers and regressors, print decision trees\n  as text or as SVG, show feature importances and explain predictions\n  of decision trees and tree-based ensembles. ELI5 understands text\n  processing utilities from scikit-learn and can highlight text data\n  accordingly. Pipeline and FeatureUnion are supported.\n  It also allows to debug scikit-learn pipelines which contain\n  HashingVectorizer, by undoing hashing.\n\n* Keras_ - explain predictions of image classifiers via Grad-CAM visualizations.\n\n* xgboost_ - show feature importances and explain predictions of XGBClassifier,\n  XGBRegressor and xgboost.Booster.\n\n* LightGBM_ - show feature importances and explain predictions of\n  LGBMClassifier and LGBMRegressor.\n\n* CatBoost_ - show feature importances of CatBoostClassifier,\n  CatBoostRegressor and catboost.CatBoost.\n\n* lightning_ - explain weights and predictions of lightning classifiers and\n  regressors.\n\n* sklearn-crfsuite_. ELI5 allows to check weights of sklearn_crfsuite.CRF\n  models.\n\n\nELI5 also implements several algorithms for inspecting black-box models\n(see `Inspecting Black-Box Estimators`_):\n\n* TextExplainer_ allows to explain predictions\n  of any text classifier using LIME_ algorithm (Ribeiro et al., 2016).\n  There are utilities for using LIME with non-text data and arbitrary black-box\n  classifiers as well, but this feature is currently experimental.\n* `Permutation importance`_ method can be used to compute feature importances\n  for black box estimators.\n\nExplanation and formatting are separated; you can get text-based explanation\nto display in console, HTML version embeddable in an IPython notebook\nor web dashboards, a ``pandas.DataFrame`` object if you want to process\nresults further, or JSON version which allows to implement custom rendering\nand formatting on a client.\n\n.. _lightning: https://github.com/scikit-learn-contrib/lightning\n.. _scikit-learn: https://github.com/scikit-learn/scikit-learn\n.. _sklearn-crfsuite: https://github.com/TeamHG-Memex/sklearn-crfsuite\n.. _LIME: https://eli5.readthedocs.io/en/latest/blackbox/lime.html\n.. _TextExplainer: https://eli5.readthedocs.io/en/latest/tutorials/black-box-text-classifiers.html\n.. _xgboost: https://github.com/dmlc/xgboost\n.. _LightGBM: https://github.com/Microsoft/LightGBM\n.. _Catboost: https://github.com/catboost/catboost\n.. _Keras: https://keras.io/\n.. _Permutation importance: https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html\n.. _Inspecting Black-Box Estimators: https://eli5.readthedocs.io/en/latest/blackbox/index.html\n\nLicense is MIT.\n\nCheck `docs \u003chttps://eli5.readthedocs.io/\u003e`_ for more.\n\n----\n\n.. image:: https://hyperiongray.s3.amazonaws.com/define-hg.svg\n\t:target: https://www.hyperiongray.com/?pk_campaign=github\u0026pk_kwd=eli5\n\t:alt: define hyperiongray\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTeamHG-Memex%2Feli5","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FTeamHG-Memex%2Feli5","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTeamHG-Memex%2Feli5/lists"}