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.
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