Skip to main content

Debug machine learning classifiers and explain their predictions

Project description

PyPI Version Build Status Code Coverage Documentation

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions.

Currently it allows to:

  • explain weights and predictions of scikit-learn linear classifiers and regressors;
  • explain weights of scikit-learn decision trees and tree-based ensemble classifiers (via feature importances);
  • debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing;
  • explain predictions of any black-box classifier using LIME ( http://arxiv.org/abs/1602.04938 ) algorithm.

TODO:

License is MIT.

Check docs for more (sorry, also TODO).

Changelog

0.0.6 (2016-10-12)

  • Candidate features in eli5.sklearn.InvertableHashingVectorizer are ordered by their frequency, first candidate is always positive.

0.0.5 (2016-09-27)

  • HashingVectorizer support in explain_prediction;
  • add an option to pass coefficient scaling array; it is useful if you want to compare coefficients for features which scale or sign is different in the input;
  • bug fix: classifier weights are no longer changed by eli5 functions.

0.0.4 (2016-09-24)

  • eli5.sklearn.InvertableHashingVectorizer and eli5.sklearn.FeatureUnhasher allow to recover feature names for pipelines which use HashingVectorizer or FeatureHasher;
  • added support for scikit-learn linear regression models (ElasticNet, Lars, Lasso, LinearRegression, LinearSVR, Ridge, SGDRegressor);
  • doc and vec arguments are swapped in explain_prediction function; vec can now be omitted if an example is already vectorized;
  • fixed issue with dense feature vectors;
  • all class_names arguments are renamed to target_names;
  • feature name guessing is fixed for scikit-learn ensemble estimators;
  • testing improvements.

0.0.3 (2016-09-21)

  • support any black-box classifier using LIME (http://arxiv.org/abs/1602.04938) algorithm; text data support is built-in;
  • “vectorized” argument for sklearn.explain_prediction; it allows to pass example which is already vectorized;
  • allow to pass feature_names explicitly;
  • support classifiers without get_feature_names method using auto-generated feature names.

0.0.2 (2016-09-19)

  • ‘top’ argument of explain_prediction can be a tuple (num_positive, num_negative);
  • classifier name is no longer printed by default;
  • added eli5.sklearn.explain_prediction to explain individual examples;
  • fixed numpy warning.

0.0.1 (2016-09-15)

Pre-release.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for eli5, version 0.0.6
Filename, size File type Python version Upload date Hashes
Filename, size eli5-0.0.6-py2.py3-none-any.whl (20.7 kB) File type Wheel Python version 3.5 Upload date Hashes View
Filename, size eli5-0.0.6.tar.gz (15.5 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page