Eli5

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* fixed scikit-learn 0.21+ support (randomized linear models are removed
from scikit-learn);
* fixed pandas.DataFrame + xgboost support for PermutationImportance;
* fixed tests with recent numpy;
* added conda install instructions (conda package is maintained by community);
* tutorial is updated to use xgboost 0.81;
* update docs to use pandoc 2.x.

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* fixed Python 3.7 support;
* added support for XGBoost > 0.6a2;
* fixed deprecation warnings in numpy >= 1.14;
* documentation, type annotation and test improvements.

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* **backwards incompatible**: DataFrame objects with explanations no longer
use indexes and pivot tables, they are now just plain DataFrames;
* new method for inspection black-box models is added
(:ref:`eli5-permutation-importance`);
* transfor_feature_names is implemented for sklearn's MinMaxScaler,
StandardScaler, MaxAbsScaler and RobustScaler;
* zero and negative feature importances are no longer hidden;
* fixed compatibility with scikit-learn 0.19;
* fixed compatibility with LightGBM master (2.0.5 and 2.0.6 are still
unsupported - there are bugs in LightGBM);
* documentation, testing and type annotation improvements.

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* better pandas.DataFrame integration: :func:`eli5.explain_weights_df`,
:func:`eli5.explain_weights_dfs`, :func:`eli5.explain_prediction_df`,
:func:`eli5.explain_prediction_dfs`,
:func:`eli5.format_as_dataframe <eli5.formatters.as_dataframe.format_as_dataframe>`
and :func:`eli5.format_as_dataframes <eli5.formatters.as_dataframe.format_as_dataframes>`
functions allow to export explanations to pandas.DataFrames;
* :func:`eli5.explain_prediction` now shows predicted class for binary
classifiers (previously it was always showing positive class);
* :func:`eli5.explain_prediction` supports ``targets=[<class>]`` now
for binary classifiers; e.g. to show result as seen for negative class,
you can use ``eli5.explain_prediction(..., targets=[False])``;
* support :func:`eli5.explain_prediction` and :func:`eli5.explain_weights`
for libsvm-based linear estimators from sklearn.svm: ``SVC(kernel='linear')``
(only binary classification), ``NuSVC(kernel='linear')`` (only
binary classification), ``SVR(kernel='linear')``, ``NuSVR(kernel='linear')``,
``OneClassSVM(kernel='linear')``;
* fixed :func:`eli5.explain_weights` for LightGBM_ estimators in Python 2 when
``importance_type`` is 'split' or 'weight';
* testing improvements.

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* Fixed :func:`eli5.explain_prediction` for recent LightGBM_ versions;
* fixed Python 3 deprecation warning in formatters.html;
* testing improvements.

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* :func:`eli5.explain_weights` and :func:`eli5.explain_prediction`
works with xgboost.Booster, not only with sklearn-like APIs;
* :func:`eli5.formatters.as_dict.format_as_dict` is now available as
``eli5.format_as_dict``;
* testing and documentation fixes.