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.

Pipeline and FeatureUnion are supported.

ELI5 understands text processing utilities from scikit-learn and can highlight text data accordingly. 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 and CatBoostRegressor.

lightning – explain weights and predictions of lightning classifiers and regressors.

sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.