Eli5

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* 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.

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

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* 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.

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* 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.

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* 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.

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* '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.