alexxnica / eli5

A library for debugging machine learning classifiers and explaining their predictions

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. It also allows to debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing.
• xgboost - show feature importances and explain predictions of XGBClassifier and XGBRegressor.
• lightning - explain weights and predictions of lightning classifiers and regressors.
• sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.

ELI5 also provides TextExplainer which 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.

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, or JSON version which allows to implement custom rendering and formatting on a client.

License is MIT.

Check docs for more.