- Update to Version 0.21.2
  + sklearn.decomposition
    * Fix:  Fixed a bug in cross_decomposition.CCA improving numerical
      stability when Y is close to zero..
  + sklearn.metrics
    * Fix:  Fixed a bug in metrics.euclidean_distances where a part of the
      distance matrix was left un-instanciated for suffiently large float32
      datasets (regression introduced in 0.21)..
  + sklearn.preprocessing
    * Fix:  Fixed a bug in preprocessing.OneHotEncoder where the new
      drop parameter was not reflected in get_feature_names..
  + sklearn.utils.sparsefuncs
    * Fix:  Fixed a bug where min_max_axis would fail on 32-bit systems
      for certain large inputs. This affects preprocessing.MaxAbsScaler,
      preprocessing.normalize and preprocessing.LabelBinarizer..
- Update to Version 0.21.1
  + sklearn.metrics
    * Fix:  Fixed a bug in metrics.pairwise_distances where it would raise
      AttributeError for boolean metrics when X had a boolean dtype and
      Y == None..
    * Fix:  Fixed two bugs in metrics.pairwise_distances when
      n_jobs > 1. First it used to return a distance matrix with same dtype as
      input, even for integer dtype. Then the diagonal was not zeros for euclidean
      metric when Y is X..
  + sklearn.neighbors
    * Fix:  Fixed a bug in neighbors.KernelDensity which could not be
      restored from a pickle if sample_weight had been used..
- Update to Version 0.21.0
  + Changed models
    The following estimators and functions, when fit with the same data and
    parameters, may produce different models from the previous version. This often
    occurs due to changes in the modelling logic (bug fixes or enhancements), or in
    random sampling procedures.
    * discriminant_analysis.LinearDiscriminantAnalysis for multiclass
      classification. |Fix|
    * discriminant_analysis.LinearDiscriminantAnalysis with 'eigen'
      solver. |Fix|
    * linear_model.BayesianRidge |Fix|
    * Decision trees and derived ensembles when both max_depth and
      max_leaf_nodes are set. |Fix|
    * linear_model.LogisticRegression and
      linear_model.LogisticRegressionCV with 'saga' solver. |Fix|
    * ensemble.GradientBoostingClassifier |Fix|
    * sklearn.feature_extraction.text.HashingVectorizer,
      sklearn.feature_extraction.text.TfidfVectorizer, and
      sklearn.feature_extraction.text.CountVectorizer |Fix|
    * neural_network.MLPClassifier |Fix|
    * svm.SVC.decision_function and
      multiclass.OneVsOneClassifier.decision_function. |Fix|
    * linear_model.SGDClassifier and any derived classifiers. |Fix|
    * Any model using the linear_model.sag.sag_solver function with a 0
      seed, including linear_model.LogisticRegression,
      linear_model.LogisticRegressionCV, linear_model.Ridge,
      and linear_model.RidgeCV with 'sag' solver. |Fix|
    * linear_model.RidgeCV when using generalized cross-validation
      with sparse inputs. |Fix|
    Details are listed in the changelog below.
    (While we are trying to better inform users by providing this information, we
    cannot assure that this list is complete.)
  + Known Major Bugs
    * The default max_iter for linear_model.LogisticRegression is too
      small for many solvers given the default tol. In particular, we
      accidentally changed the default max_iter for the liblinear solver from
      1000 to 100 iterations in released in version 0.16.
      In a future release we hope to choose better default max_iter and tol
      heuristically depending on the solver.
  + Support for Python 3.4 and below has been officially dropped.
  + sklearn.base
    * API:  The R2 score used when calling score on a regressor will use
      multioutput='uniform_average' from version 0.23 to keep consistent with
      metrics.r2_score. This will influence the score method of all
      the multioutput regressors (except for
      multioutput.MultiOutputRegressor)..
  + sklearn.calibration
    * Enhancement:  Added support to bin the data passed into
      calibration.calibration_curve by quantiles instead of uniformly
      between 0 and 1..
    * Enhancement:  Allow n-dimensional arrays as input for
      calibration.CalibratedClassifierCV..
  + sklearn.cluster
    * MajorFeature:  A new clustering algorithm: cluster.OPTICS: an
      algoritm related to cluster.DBSCAN, that has hyperparameters easier
      to set and that scales better,
    * Fix:  Fixed a bug where cluster.Birch could occasionally raise an
      AttributeError..
    * Fix:  Fixed a bug in cluster.KMeans where empty clusters weren't
      correctly relocated when using sample weights..
    * API:  The n_components_ attribute in cluster.AgglomerativeClustering
      and cluster.FeatureAgglomeration has been renamed to
      n_connected_components_..
    * Enhancement:  cluster.AgglomerativeClustering and
      cluster.FeatureAgglomeration now accept a distance_threshold
      parameter which can be used to find the clusters instead of n_clusters.
  + sklearn.compose
    * API:  compose.ColumnTransformer is no longer an experimental
      feature..
  + sklearn.datasets
    * Fix:  Added support for 64-bit group IDs and pointers in SVMLight files..
    * Fix:  datasets.load_sample_images returns images with a deterministic
      order..
  + sklearn.decomposition
    * Enhancement:  decomposition.KernelPCA now has deterministic output
      (resolved sign ambiguity in eigenvalue decomposition of the kernel matrix)..
    * Fix:  Fixed a bug in decomposition.KernelPCA, fit().transform()
      now produces the correct output (the same as fit_transform()) in case
      of non-removed zero eigenvalues (remove_zero_eig=False).
      fit_inverse_transform was also accelerated by using the same trick as
      fit_transform to compute the transform of X.
    * Fix:  Fixed a bug in decomposition.NMF where init = 'nndsvd',
      init = 'nndsvda', and init = 'nndsvdar' are allowed when
      n_components < n_features instead of
      n_components <= min(n_samples, n_features).
    * API:  The default value of the init argument in
      decomposition.non_negative_factorization will change from
      random to None in version 0.23 to make it consistent with
      decomposition.NMF. A FutureWarning is raised when
      the default value is used..
  + sklearn.discriminant_analysis
    * Enhancement:  discriminant_analysis.LinearDiscriminantAnalysis now
      preserves float32 and float64 dtypes.
    * Fix:  A ChangedBehaviourWarning is now raised when
      discriminant_analysis.LinearDiscriminantAnalysis is given as
      parameter n_components > min(n_features, n_classes - 1), and
      n_components is changed to min(n_features, n_classes - 1) if so.
      Previously the change was made, but silently..
    * Fix:  Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis
      where the predicted probabilities would be incorrectly computed in the
      multiclass case.
    * Fix:  Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis
      where the predicted probabilities would be incorrectly computed with eigen
      solver.
  + sklearn.dummy
    * Fix:  Fixed a bug in dummy.DummyClassifier where the
      predict_proba method was returning int32 array instead of
      float64 for the stratified strategy..
    * Fix:  Fixed a bug in dummy.DummyClassifier where it was throwing a
      dimension mismatch error in prediction time if a column vector y with
      shape=(n, 1) was given at fit time.
  + sklearn.ensemble
    * MajorFeature:  Add two new implementations of
      gradient boosting trees: ensemble.HistGradientBoostingClassifier
      and ensemble.HistGradientBoostingRegressor. The implementation of
      these estimators is inspired by
      LightGBM and can be orders of
      magnitude faster than ensemble.GradientBoostingRegressor and
      ensemble.GradientBoostingClassifier when the number of samples is
      larger than tens of thousands of samples. The API of these new estimators
      is slightly different, and some of the features from
      ensemble.GradientBoostingClassifier and
      ensemble.GradientBoostingRegressor are not yet supported.
      These new estimators are experimental, which means that their results or
      their API might change without any deprecation cycle. To use them, you
      need to explicitly import enable_hist_gradient_boosting::
        >>> # explicitly require this experimental feature
        >>> from sklearn.experimental import enable_hist_gradient_boosting  # noqa
        >>> # now you can import normally from sklearn.ensemble
        >>> from sklearn.ensemble import HistGradientBoostingClassifier.
    * Feature:  Add ensemble.VotingRegressor
      which provides an equivalent of ensemble.VotingClassifier
      for regression problems.
    * Efficiency:  Make ensemble.IsolationForest prefer threads over
      processes when running with n_jobs > 1 as the underlying decision tree
      fit calls do release the GIL. This changes reduces memory usage and
      communication overhead.
    * Efficiency:  Make ensemble.IsolationForest more memory efficient
      by avoiding keeping in memory each tree prediction..
    * Efficiency:  ensemble.IsolationForest now uses chunks of data at
      prediction step, thus capping the memory usage..
    * Efficiency:  sklearn.ensemble.GradientBoostingClassifier and
      sklearn.ensemble.GradientBoostingRegressor now keep the
      input y as float64 to avoid it being copied internally by trees..
    * Enhancement:  Minimized the validation of X in
      ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor.
    * Enhancement:  ensemble.IsolationForest now exposes warm_start
      parameter, allowing iterative addition of trees to an isolation
      forest..
    * Fix:  The values of feature_importances_ in all random forest based
      models (i.e.
      ensemble.RandomForestClassifier,
      ensemble.RandomForestRegressor,
      ensemble.ExtraTreesClassifier,
      ensemble.ExtraTreesRegressor,
      ensemble.RandomTreesEmbedding,
      ensemble.GradientBoostingClassifier, and
      ensemble.GradientBoostingRegressor) now:
      > sum up to 1
      > all the single node trees in feature importance calculation are ignored
      > in case all trees have only one single node (i.e. a root node),
        feature importances will be an array of all zeros.
    * Fix:  Fixed a bug in ensemble.GradientBoostingClassifier and
      ensemble.GradientBoostingRegressor, which didn't support
      scikit-learn estimators as the initial estimator. Also added support of
      initial estimator which does not support sample weights. and.
    * Fix:  Fixed the output of the average path length computed in
      ensemble.IsolationForest when the input is either 0, 1 or 2.
    * Fix:  Fixed a bug in ensemble.GradientBoostingClassifier where
      the gradients would be incorrectly computed in multiclass classification
      problems..
    * Fix:  Fixed a bug in ensemble.GradientBoostingClassifier where
      validation sets for early stopping were not sampled with stratification..
    * Fix:  Fixed a bug in ensemble.GradientBoostingClassifier where
      the default initial prediction of a multiclass classifier would predict the
      classes priors instead of the log of the priors..
    * Fix:  Fixed a bug in ensemble.RandomForestClassifier where the
      predict method would error for multiclass multioutput forests models
      if any targets were strings..
    * Fix:  Fixed a bug in ensemble.gradient_boosting.LossFunction and
      ensemble.gradient_boosting.LeastSquaresError where the default
      value of learning_rate in update_terminal_regions is not consistent
      with the document and the caller functions. Note however that directly using
      these loss functions is deprecated..
    * Fix:  ensemble.partial_dependence (and consequently the new
      version sklearn.inspection.partial_dependence) now takes sample
      weights into account for the partial dependence computation when the
      gradient boosting model has been trained with sample weights..
    * API:  ensemble.partial_dependence and
      ensemble.plot_partial_dependence are now deprecated in favor of
      inspection.partial_dependence
      and
      inspection.plot_partial_dependence. and
    * Fix:  ensemble.VotingClassifier and
      ensemble.VotingRegressor were failing during fit in one
      of the estimators was set to None and sample_weight was not None..
    * API:  ensemble.VotingClassifier and
      ensemble.VotingRegressor accept 'drop' to disable an estimator
      in addition to None to be consistent with other estimators (i.e.,
      pipeline.FeatureUnion and compose.ColumnTransformer)..
  + sklearn.externals
    * API:  Deprecated externals.six since we have dropped support for
      Python 2.7..
  + sklearn.feature_extraction
    * Fix:  If input='file' or input='filename', and a callable is given as
      the analyzer, sklearn.feature_extraction.text.HashingVectorizer,
      sklearn.feature_extraction.text.TfidfVectorizer, and
      sklearn.feature_extraction.text.CountVectorizer now read the data
      from the file(s) and then pass it to the given analyzer, instead of
      passing the file name(s) or the file object(s) to the analyzer..
  + sklearn.impute
    * MajorFeature:  Added impute.IterativeImputer, which is a strategy
      for imputing missing values by modeling each feature with missing values as a
      function of other features in a round-robin fashion.
      The API of IterativeImputer is experimental and subject to change without any
      deprecation cycle. To use them, you need to explicitly import
      enable_iterative_imputer::
        >>> from sklearn.experimental import enable_iterative_imputer  # noqa
        >>> # now you can import normally from sklearn.impute
        >>> from sklearn.impute import IterativeImputer
    * Feature:  The impute.SimpleImputer and
      impute.IterativeImputer have a new parameter 'add_indicator',
      which simply stacks a impute.MissingIndicator transform into the
      output of the imputer's transform. That allows a predictive estimator to
      account for missingness.
    * Fix:  In impute.MissingIndicator avoid implicit densification by
      raising an exception if input is sparse add missing_values property
      is set to 0..
    * Fix:  Fixed two bugs in impute.MissingIndicator. First, when
      X is sparse, all the non-zero non missing values used to become
      explicit False in the transformed data. Then, when
      features='missing-only', all features used to be kept if there were no
      missing values at all..
  + sklearn.inspection
    (new subpackage)
    * Feature:  Partial dependence plots
      (inspection.plot_partial_dependence) are now supported for
      any regressor or classifier (provided that they have a predict_proba
      method).
  + sklearn.isotonic
    * Feature:  Allow different dtypes (such as float32) in
      isotonic.IsotonicRegression.
  + sklearn.linear_model
    * Enhancement:  linear_model.Ridge now preserves float32 and
      float64 dtypes.
    * Feature:  linear_model.LogisticRegression and
      linear_model.LogisticRegressionCV now support Elastic-Net penalty,
      with the 'saga' solver..
    * Feature:  Added linear_model.lars_path_gram, which is
      linear_model.lars_path in the sufficient stats mode, allowing
      users to compute linear_model.lars_path without providing
      X and y..
    * Efficiency:  linear_model.make_dataset now preserves
      float32 and float64 dtypes, reducing memory consumption in stochastic
      gradient, SAG and SAGA solvers.
    * Enhancement:  linear_model.LogisticRegression now supports an
      unregularized objective when penalty='none' is passed. This is
      equivalent to setting C=np.inf with l2 regularization. Not supported
      by the liblinear solver..
    * Enhancement:  sparse_cg solver in linear_model.Ridge
      now supports fitting the intercept (i.e. fit_intercept=True) when
      inputs are sparse..
    * Enhancement:  The coordinate descent solver used in Lasso, ElasticNet,
      etc. now issues a ConvergenceWarning when it completes without meeting the
      desired toleranbce.
    * Fix:  Fixed a bug in linear_model.LogisticRegression and
      linear_model.LogisticRegressionCV with 'saga' solver, where the
      weights would not be correctly updated in some cases..
    * Fix:  Fixed the posterior mean, posterior covariance and returned
      regularization parameters in linear_model.BayesianRidge. The
      posterior mean and the posterior covariance were not the ones computed
      with the last update of the regularization parameters and the returned
      regularization parameters were not the final ones. Also fixed the formula of
      the log marginal likelihood used to compute the score when
      compute_score=True..
    * Fix:  Fixed a bug in linear_model.LassoLarsIC, where user input
      copy_X=False at instance creation would be overridden by default
      parameter value copy_X=True in fit.
    * Fix:  Fixed a bug in linear_model.LinearRegression that
      was not returning the same coeffecients and intercepts with
      fit_intercept=True in sparse and dense case.
    * Fix:  Fixed a bug in linear_model.HuberRegressor that was
      broken when X was of dtype bool..
    * Fix:  Fixed a performance issue of saga and sag solvers when called
      in a joblib.Parallel setting with n_jobs > 1 and
      backend="threading", causing them to perform worse than in the sequential
      case..
    * Fix:  Fixed a bug in
      linear_model.stochastic_gradient.BaseSGDClassifier that was not
      deterministic when trained in a multi-class setting on several threads..
    * Fix:  Fixed bug in linear_model.ridge_regression,
      linear_model.Ridge and
      linear_model.RidgeClassifier that
      caused unhandled exception for arguments return_intercept=True and
      solver=auto (default) or any other solver different from sag.
    * Fix:  linear_model.ridge_regression will now raise an exception
      if return_intercept=True and solver is different from sag. Previously,
      only warning was issued.
    * Fix:  linear_model.ridge_regression will choose sparse_cg
      solver for sparse inputs when solver=auto and sample_weight
      is provided (previously cholesky solver was selected).
    * API:   The use of linear_model.lars_path with X=None
      while passing Gram is deprecated in version 0.21 and will be removed
      in version 0.23. Use linear_model.lars_path_gram instead..
    * API:  linear_model.logistic_regression_path is deprecated
      in version 0.21 and will be removed in version 0.23..
    * Fix:  linear_model.RidgeCV with generalized cross-validation
      now correctly fits an intercept when fit_intercept=True and the design
      matrix is sparse.
  + sklearn.manifold
    * Efficiency:  Make manifold.tsne.trustworthiness use an inverted index
      instead of an np.where lookup to find the rank of neighbors in the input
      space. This improves efficiency in particular when computed with
      lots of neighbors and/or small datasets..
  + sklearn.metrics
    * Feature:  Added the metrics.max_error metric and a corresponding
      'max_error' scorer for single output regression..
    * Feature:  Add metrics.multilabel_confusion_matrix, which calculates a
      confusion matrix with true positive, false positive, false negative and true
      negative counts for each class. This facilitates the calculation of set-wise
      metrics such as recall, specificity, fall out and miss rate.
    * Feature:  metrics.jaccard_score has been added to calculate the
      Jaccard coefficient as an evaluation metric for binary, multilabel and
      multiclass tasks, with an interface analogous to metrics.f1_score.
    * Feature:  Added metrics.pairwise.haversine_distances which can be
      accessed with metric='pairwise' through metrics.pairwise_distances
      and estimators. (Haversine distance was previously available for nearest
      neighbors calculation.)
    * Efficiency:  Faster metrics.pairwise_distances with n_jobs
      > 1 by using a thread-based backend, instead of process-based backends.
    * Efficiency:  The pairwise manhattan distances with sparse input now uses the
      BLAS shipped with scipy instead of the bundled BLAS.
    * Enhancement:  Use label accuracy instead of micro-average on
      metrics.classification_report to avoid confusion. micro-average is
      only shown for multi-label or multi-class with a subset of classes because
      it is otherwise identical to accuracy.
    * Enhancement:  Added beta parameter to
      metrics.homogeneity_completeness_v_measure and
      metrics.v_measure_score to configure the
      tradeoff between homogeneity and completeness.
    * Fix:  The metric metrics.r2_score is degenerate with a single sample
      and now it returns NaN and raises exceptions.UndefinedMetricWarning..
    * Fix:  Fixed a bug where metrics.brier_score_loss will sometimes
      return incorrect result when there's only one class in y_true..
    * Fix:  Fixed a bug in metrics.label_ranking_average_precision_score
      where sample_weight wasn't taken into account for samples with degenerate
      labels..
    * API:  The parameter labels in metrics.hamming_loss is deprecated
      in version 0.21 and will be removed in version 0.23.
    * Fix:  The function metrics.pairwise.euclidean_distances, and
      therefore several estimators with metric='euclidean', suffered from
      numerical precision issues with float32 features. Precision has been
      increased at the cost of a small drop of performance.
    * API:  metrics.jaccard_similarity_score is deprecated in favour of
      the more consistent metrics.jaccard_score. The former behavior for
      binary and multiclass targets is broken..
  + sklearn.mixture
    * Fix:  Fixed a bug in mixture.BaseMixture and therefore on estimators
      based on it, i.e. mixture.GaussianMixture and
      mixture.BayesianGaussianMixture, where fit_predict and
      fit.predict were not equivalent..
  + sklearn.model_selection
    * Feature:  Classes ~model_selection.GridSearchCV and
      ~model_selection.RandomizedSearchCV now allow for refit=callable
      to add flexibility in identifying the best estimator.
      See sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py.
    * Enhancement:  Classes ~model_selection.GridSearchCV,
      ~model_selection.RandomizedSearchCV, and methods
      ~model_selection.cross_val_score,
      ~model_selection.cross_val_predict,
      ~model_selection.cross_validate, now print train scores when
      return_train_scores is True and verbose > 2. For
      ~model_selection.learning_curve, and
      ~model_selection.validation_curve only the latter is required.
    * Enhancement:  Some CV splitter classes and
      model_selection.train_test_split now raise ValueError when the
      resulting training set is empty..
    * Fix:  Fixed a bug where model_selection.StratifiedKFold
      shuffles each class's samples with the same random_state,
      making shuffle=True ineffective..
    * Fix:  Added ability for model_selection.cross_val_predict to handle
      multi-label (and multioutput-multiclass) targets with predict_proba-type
      methods..
    * Fix:  Fixed an issue in ~model_selection.cross_val_predict where
      method="predict_proba" returned always 0.0 when one of the classes was
      excluded in a cross-validation fold.
  + sklearn.multiclass
    * Fix:  Fixed an issue in multiclass.OneVsOneClassifier.decision_function
      where the decision_function value of a given sample was different depending on
      whether the decision_function was evaluated on the sample alone or on a batch
      containing this same sample due to the scaling used in decision_function..
  + sklearn.multioutput
    * Fix:  Fixed a bug in multioutput.MultiOutputClassifier where the
      predict_proba method incorrectly checked for predict_proba attribute in
      the estimator object.
  + sklearn.neighbors
    * MajorFeature:  Added neighbors.NeighborhoodComponentsAnalysis for
      metric learning, which implements the Neighborhood Components Analysis
      algorithm.
    * API:  Methods in neighbors.NearestNeighbors :
      ~neighbors.NearestNeighbors.kneighbors,
      ~neighbors.NearestNeighbors.radius_neighbors,
      ~neighbors.NearestNeighbors.kneighbors_graph,
      ~neighbors.NearestNeighbors.radius_neighbors_graph
      now raise NotFittedError, rather than AttributeError,
      when called before fit.
  + sklearn.neural_network
    * Fix:  Fixed a bug in neural_network.MLPClassifier and
      neural_network.MLPRegressor where the option shuffle=False
      was being ignored..
    * Fix:  Fixed a bug in neural_network.MLPClassifier where
      validation sets for early stopping were not sampled with stratification. In
      the multilabel case however, splits are still not stratified..
  + sklearn.pipeline
    * Feature:  pipeline.Pipeline can now use indexing notation (e.g.
      my_pipeline[0:-1]) to extract a subsequence of steps as another Pipeline
      instance.  A Pipeline can also be indexed directly to extract a particular
      step (e.g. my_pipeline['svc']), rather than accessing named_steps..
    * Feature:  Added optional parameter verbose in pipeline.Pipeline,
      compose.ColumnTransformer and pipeline.FeatureUnion
      and corresponding make_ helpers for showing progress and timing of
      each step.
    * Enhancement:  pipeline.Pipeline now supports using 'passthrough'
      as a transformer, with the same effect as None..
    * Enhancement:  pipeline.Pipeline  implements __len__ and
      therefore len(pipeline) returns the number of steps in the pipeline..
  + sklearn.preprocessing
    * Feature:  preprocessing.OneHotEncoder now supports dropping one
      feature per category with a new drop parameter..
    * Efficiency:  preprocessing.OneHotEncoder and
      preprocessing.OrdinalEncoder now handle pandas DataFrames more
      efficiently..
    * Efficiency:  Make preprocessing.MultiLabelBinarizer cache class
      mappings instead of calculating it every time on the fly.
    * Efficiency:  preprocessing.PolynomialFeatures now supports
      compressed sparse row (CSR) matrices as input for degrees 2 and 3. This is
      typically much faster than the dense case as it scales with matrix density
      and expansion degree (on the order of density^degree), and is much, much
      faster than the compressed sparse column (CSC) case..
    * Efficiency:  Speed improvement in preprocessing.PolynomialFeatures,
      in the dense case. Also added a new parameter order which controls output
      order for further speed performances..
    * Fix:  Fixed the calculation overflow when using a float16 dtype with
      preprocessing.StandardScaler.
    * Fix:  Fixed a bug in preprocessing.QuantileTransformer and
      preprocessing.quantile_transform to force n_quantiles to be at most
      equal to n_samples. Values of n_quantiles larger than n_samples were either
      useless or resulting in a wrong approximation of the cumulative distribution
      function estimator..
    * API:  The default value of copy in preprocessing.quantile_transform
      will change from False to True in 0.23 in order to make it more consistent
      with the default copy values of other functions in
      preprocessing and prevent unexpected side effects by modifying
      the value of X inplace..
  + sklearn.svm
    * Fix:  Fixed an issue in svm.SVC.decision_function when
      decision_function_shape='ovr'. The decision_function value of a given
      sample was different depending on whether the decision_function was evaluated
      on the sample alone or on a batch containing this same sample due to the
      scaling used in decision_function..
  + sklearn.tree
    * Feature:  Decision Trees can now be plotted with matplotlib using
      tree.plot_tree without relying on the dot library,
      removing a hard-to-install dependency..
    * Feature:  Decision Trees can now be exported in a human readable
      textual format using tree.export_text.
    * Feature:  get_n_leaves() and get_depth() have been added to
      tree.BaseDecisionTree and consequently all estimators based
      on it, including tree.DecisionTreeClassifier,
      tree.DecisionTreeRegressor, tree.ExtraTreeClassifier,
      and tree.ExtraTreeRegressor..
    * Fix:  Trees and forests did not previously predict multi-output
      classification targets with string labels, despite accepting them in fit..
    * Fix:  Fixed an issue with tree.BaseDecisionTree
      and consequently all estimators based
      on it, including tree.DecisionTreeClassifier,
      tree.DecisionTreeRegressor, tree.ExtraTreeClassifier,
      and tree.ExtraTreeRegressor, where they used to exceed the given
      max_depth by 1 while expanding the tree if max_leaf_nodes and
      max_depth were both specified by the user. Please note that this also
      affects all ensemble methods using decision trees..
  + sklearn.utils
    * Feature:  utils.resample now accepts a stratify parameter for
      sampling according to class distributions..
    * API:  Deprecated warn_on_dtype parameter from utils.check_array
      and utils.check_X_y. Added explicit warning for dtype conversion
      in check_pairwise_arrays if the metric being passed is a
      pairwise boolean metric..
  + Multiple modules
    * MajorFeature:  The __repr__() method of all estimators (used when calling
      print(estimator)) has been entirely re-written, building on Python's
      pretty printing standard library. All parameters are printed by default,
      but this can be altered with the print_changed_only option in
      sklearn.set_config..
    * MajorFeature:  Add estimators tags: these are annotations of estimators
      that allow programmatic inspection of their capabilities, such as sparse
      matrix support, supported output types and supported methods. Estimator
      tags also determine the tests that are run on an estimator when
      check_estimator is called.
    * Efficiency:  Memory copies are avoided when casting arrays to a different
      dtype in multiple estimators..
    * Fix:  Fixed a bug in the implementation of the our_rand_r
      helper function that was not behaving consistently across platforms.
  + Miscellaneous
    * Enhancement:  Joblib is no longer vendored in scikit-learn, and becomes a
      dependency. Minimal supported version is joblib 0.11, however using
      version >= 0.13 is strongly recommended..
  + Changes to estimator checks
    These changes mostly affect library developers.
    * Add check_fit_idempotent to
      ~utils.estimator_checks.check_estimator, which checks that
      when fit is called twice with the same data, the ouput of
      predict, predict_proba, transform, and decision_function does not
      change.
    * Many checks can now be disabled or configured with estimator_tags..

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