Eli5

----------------

* :func:`eli5.explain_prediction`: new 'top_targets' argument allows
to display only predictions with highest or lowest scores;
* :func:`eli5.explain_weights` allows to customize the way feature importances
are computed for XGBClassifier and XGBRegressor using ``importance_type``
argument (see docs for the :ref:`eli5 XGBoost support <library-xgboost>`);
* :func:`eli5.explain_weights` uses gain for XGBClassifier and XGBRegressor
feature importances by default; this method is a better indication of
what's going, and it makes results more compatible with feature importances
displayed for scikit-learn gradient boosting methods.

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* packaging fix: scikit-learn is added to install_requires in setup.py.

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* :func:`eli5.explain_prediction` works for XGBClassifier, XGBRegressor
from XGBoost and for ExtraTreesClassifier, ExtraTreesRegressor,
GradientBoostingClassifier, GradientBoostingRegressor,
RandomForestClassifier, RandomForestRegressor, DecisionTreeClassifier
and DecisionTreeRegressor from scikit-learn.
Explanation method is based on
http://blog.datadive.net/interpreting-random-forests/ .
* :func:`eli5.explain_weights` now supports tree-based regressors from
scikit-learn: DecisionTreeRegressor, AdaBoostRegressor,
GradientBoostingRegressor, RandomForestRegressor and ExtraTreesRegressor.
* :func:`eli5.explain_weights` works for XGBRegressor;
* new :ref:`TextExplainer <lime-tutorial>` class allows to explain predictions
of black-box text classification pipelines using LIME algorithm;
many improvements in :ref:`eli5.lime <eli5-lime>`.
* better ``sklearn.pipeline.FeatureUnion`` support in
:func:`eli5.explain_prediction`;
* rendering performance is improved;
* a number of remaining feature importances is shown when the feature
importance table is truncated;
* styling of feature importances tables is fixed;
* :func:`eli5.explain_weights` and :func:`eli5.explain_prediction` support
more linear estimators from scikit-learn: HuberRegressor, LarsCV, LassoCV,
LassoLars, LassoLarsCV, LassoLarsIC, OrthogonalMatchingPursuit,
OrthogonalMatchingPursuitCV, PassiveAggressiveRegressor,
RidgeClassifier, RidgeClassifierCV, TheilSenRegressor.
* text-based formatting of decision trees is changed: for binary
classification trees only a probability of "true" class is printed,
not both probabilities as it was before.
* :func:`eli5.explain_weights` supports ``feature_filter`` in addition
to ``feature_re`` for filtering features, and :func:`eli5.explain_prediction`
now also supports both of these arguments;
* 'Weight' column is renamed to 'Contribution' in the output of
:func:`eli5.explain_prediction`;
* new ``show_feature_values=True`` formatter argument allows to display
input feature values;
* fixed an issue with analyzer='char_wb' highlighting at the start of the
text.

----------------

* XGBClassifier support (from `XGBoost <https://github.com/dmlc/xgboost>`__
package);
* :func:`eli5.explain_weights` support for sklearn OneVsRestClassifier;
* std deviation of feature importances is no longer printed as zero
if it is not available.

------------------

* packaging fixes: require attrs > 16.0.0, fixed README rendering

----------------

* HTML output;
* IPython integration;
* JSON output;
* visualization of scikit-learn text vectorizers;
* `sklearn-crfsuite <https://github.com/TeamHG-Memex/sklearn-crfsuite>`__
support;
* `lightning <https://github.com/scikit-learn-contrib/lightning>`__ support;
* :func:`eli5.show_weights` and :func:`eli5.show_prediction` functions;
* :func:`eli5.explain_weights` and :func:`eli5.explain_prediction`
functions;
* :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.