Skip to main content

Debug machine learning classifiers and explain their predictions

Project description


.. image::
:alt: PyPI Version

.. image::
:alt: Build Status

.. image::
:alt: Code Coverage

.. image::
:alt: Documentation

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 of random forests. 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.

* lightning_ - explain weights and predictions of lightning classifiers and

* sklearn-crfsuite_. ELI5 allows to check weights of sklearn_crfsuite.CRF

ELI5 also provides an alternative implementation of LIME_ algorithm,
which allows to explain predictions of any black-box classifier. 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.

.. _lightning:
.. _scikit-learn:
.. _sklearn-crfsuite:
.. _LIME:

License is MIT.

Check `docs <>`_ for more.


0.1 (2016-11-24)

* HTML output;
* IPython integration;
* JSON output;
* visualization of scikit-learn text vectorizers;
* `sklearn-crfsuite <>`_
* `lightning <>`_ support;
* :func:`eli5.show_weights` and :func:`eli5.show_prediction` functions;
* :func:`eli5.explain_weights` and :func:`eli5.explain_prediction`
* :ref:`eli5.lime <eli5-lime>` improvements: samplers for non-text data,
bug fixes, docs;
* HashingVectorizer is supported for regression tasks;
* performance improvements - feature names are lazy;
* sklearn ElasticNetCV and RidgeCV support;
* it is now possible to customize formatting output - show/hide sections,
change layout;
* sklearn OneVsRestClassifier support;
* sklearn DecisionTreeClassifier visualization (text-based or svg-based);
* dropped support for scikit-learn < 0.18;
* basic mypy type annotations;
* ``feature_re`` argument allows to show only a subset of features;
* ``target_names`` argument allows to change display names of targets/classes;
* ``targets`` argument allows to show a subset of targets/classes and
change their display order;
* documentation, more examples.

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 (
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)


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 0.1
Filename, size File type Python version Upload date Hashes
Filename, size eli5-0.1-py2.py3-none-any.whl (53.1 kB) File type Wheel Python version 3.5 Upload date Hashes View
Filename, size eli5-0.1.tar.gz (111.1 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