ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions.
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.
- 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 and explain predictions of CatBoostClassifier and CatBoostRegressor.
- 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.
License is MIT.
Check docs for more.
