ELI5
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.
-
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.
License is MIT.
Check docs for more.
