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# Version 0.20.4

July 30, 2019

This is a bug-fix release with some bug fixes applied to version 0.20.3.

## Changelog

The bundled version of joblib was upgraded from 0.13.0 to 0.13.2.

### `sklearn.cluster`

- Fix Fixed a bug in `cluster.KMeans` where KMeans++ initialisation could rarely result in an IndexError. #11756 by Joel Nothman.

### `sklearn.compose`

- Fix Fixed an issue in `compose.ColumnTransformer` where using DataFrames whose column order differs between :func:`fit` and :func:`transform` could lead to silently passing incorrect columns to the `remainder` transformer. #14237 by `Andreas Schuderer <schuderer>`.

### `sklearn.decomposition`

- Fix Fixed a bug in `cross_decomposition.CCA` improving numerical stability when `Y` is close to zero. #13903 by Thomas Fan.

### `sklearn.model_selection`

- Fix Fixed a bug where `model_selection.StratifiedKFold` shuffles each class’s samples with the same `random_state`, making `shuffle=True` ineffective. #13124 by Hanmin Qin.

### `sklearn.neighbors`

- Fix Fixed a bug in `neighbors.KernelDensity` which could not be restored from a pickle if `sample_weight` had been used. #13772 by Aditya Vyas.

# Version 0.20.3

March 1, 2019

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0.

## Changelog

### `sklearn.cluster`

- Fix Fixed a bug in `cluster.KMeans` where computation was single threaded when `n_jobs > 1` or `n_jobs = -1`. #12949 by Prabakaran Kumaresshan.

### `sklearn.compose`

- Fix Fixed a bug in `compose.ColumnTransformer` to handle negative indexes in the columns list of the transformers. #12946 by Pierre Tallotte.

### `sklearn.covariance`

- Fix Fixed a regression in `covariance.graphical_lasso` so that the case `n_features=2` is handled correctly. #13276 by Aurélien Bellet.

### `sklearn.decomposition`

- Fix Fixed a bug in `decomposition.sparse_encode` where computation was single threaded when `n_jobs > 1` or `n_jobs = -1`. #13005 by Prabakaran Kumaresshan.

### `sklearn.datasets`

- Efficiency `sklearn.datasets.fetch_openml` now loads data by streaming, avoiding high memory usage. #13312 by Joris Van den Bossche.

### `sklearn.feature_extraction`

- Fix Fixed a bug in `feature_extraction.text.CountVectorizer` which would result in the sparse feature matrix having conflicting `indptr` and `indices` precisions under very large vocabularies. #11295 by Gabriel Vacaliuc.

### `sklearn.impute`

- Fix add support for non-numeric data in `sklearn.impute.MissingIndicator` which was not supported while `sklearn.impute.SimpleImputer` was supporting this for some imputation strategies. #13046 by Guillaume Lemaitre.

### `sklearn.linear_model`

- Fix Fixed a bug in `linear_model.MultiTaskElasticNet` and `linear_model.MultiTaskLasso` which were breaking when `warm_start = True`. #12360 by Aakanksha Joshi.

### `sklearn.preprocessing`

- Fix Fixed a bug in `preprocessing.KBinsDiscretizer` where `strategy='kmeans'` fails with an error during transformation due to unsorted bin edges. #13134 by Sandro Casagrande.

- Fix Fixed a bug in `preprocessing.OneHotEncoder` where the deprecation of `categorical_features` was handled incorrectly in combination with `handle_unknown='ignore'`. #12881 by Joris Van den Bossche.

- Fix Bins whose width are too small (i.e., <= 1e-8) are removed with a warning in `preprocessing.KBinsDiscretizer`. #13165 by Hanmin Qin.

### `sklearn.svm`

- Fix Fixed a bug in `svm.SVC`, `svm.NuSVC`, `svm.SVR`, `svm.NuSVR` and `svm.OneClassSVM` where the `scale` option of parameter `gamma` is erroneously defined as `1 / (n_features * X.std())`. It’s now defined as `1 / (n_features * X.var())`. #13221 by Hanmin Qin.

## Code and Documentation Contributors

With thanks to:

Adrin Jalali, Agamemnon Krasoulis, Albert Thomas, Andreas Mueller, Aurélien Bellet, bertrandhaut, Bharat Raghunathan, Dowon, Emmanuel Arias, Fibinse Xavier, Finn O’Shea, Gabriel Vacaliuc, Gael Varoquaux, Guillaume Lemaitre, Hanmin Qin, joaak, Joel Nothman, Joris Van den Bossche, Jérémie Méhault, kms15, Kossori Aruku, Lakshya KD, maikia, Manuel López-Ibáñez, Marco Gorelli, MarcoGorelli, mferrari3, Mickaël Schoentgen, Nicolas Hug, pavlos kallis, Pierre Glaser, pierretallotte, Prabakaran Kumaresshan, Reshama Shaikh, Rohit Kapoor, Roman Yurchak, SandroCasagrande, Tashay Green, Thomas Fan, Vishaal Kapoor, Zhuyi Xue, Zijie (ZJ) Poh

# Version 0.20.2

December 20, 2018

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0.

## 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.

- `sklearn.neighbors` when `metric=='jaccard'` (bug fix)

- use of `'seuclidean'` or `'mahalanobis'` metrics in some cases (bug fix)

## Changelog

### `sklearn.compose`

- Fix Fixed an issue in `compose.make_column_transformer` which raises unexpected error when columns is pandas Index or pandas Series. #12704 by Hanmin Qin.

### `sklearn.metrics`

- Fix Fixed a bug in `metrics.pairwise_distances` and `metrics.pairwise_distances_chunked` where parameters `V` of `"seuclidean"` and `VI` of `"mahalanobis"` metrics were computed after the data was split into chunks instead of being pre-computed on whole data. #12701 by Jeremie du Boisberranger.

### `sklearn.neighbors`

- Fix Fixed `sklearn.neighbors.DistanceMetric` jaccard distance function to return 0 when two all-zero vectors are compared. #12685 by Thomas Fan.

### `sklearn.utils`

- Fix Calling `utils.check_array` on `pandas.Series` with categorical data, which raised an error in 0.20.0, now returns the expected output again. #12699 by Joris Van den Bossche.

## Code and Documentation Contributors

With thanks to:

adanhawth, Adrin Jalali, Albert Thomas, Andreas Mueller, Dan Stine, Feda Curic, Hanmin Qin, Jan S, jeremiedbb, Joel Nothman, Joris Van den Bossche, josephsalmon, Katrin Leinweber, Loic Esteve, Muhammad Hassaan Rafique, Nicolas Hug, Olivier Grisel, Paul Paczuski, Reshama Shaikh, Sam Waterbury, Shivam Kotwalia, Thomas Fan

# Version 0.20.1

November 21, 2018

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0. Note that we also include some API changes in this release, so you might get some extra warnings after updating from 0.20.0 to 0.20.1.

## 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.

- `decomposition.IncrementalPCA` (bug fix)

## Changelog

### `sklearn.cluster`

- Efficiency make `cluster.MeanShift` no longer try to do nested parallelism as the overhead would hurt performance significantly when `n_jobs > 1`. #12159 by Olivier Grisel.

- Fix Fixed a bug in `cluster.DBSCAN` with precomputed sparse neighbors graph, which would add explicitly zeros on the diagonal even when already present. #12105 by Tom Dupre la Tour.

### `sklearn.compose`

- Fix Fixed an issue in `compose.ColumnTransformer` when stacking columns with types not convertible to a numeric. #11912 by Adrin Jalali.

- API Change `compose.ColumnTransformer` now applies the `sparse_threshold` even if all transformation results are sparse. #12304 by Andreas Müller.

- API Change `compose.make_column_transformer` now expects `(transformer, columns)` instead of `(columns, transformer)` to keep consistent with `compose.ColumnTransformer`. #12339 by Adrin Jalali.

### `sklearn.datasets`

- Fix `datasets.fetch_openml` to correctly use the local cache. #12246 by Jan N. van Rijn.

- Fix `datasets.fetch_openml` to correctly handle ignore attributes and row id attributes. #12330 by Jan N. van Rijn.

- Fix Fixed integer overflow in `datasets.make_classification` for values of `n_informative` parameter larger than 64. #10811 by Roman Feldbauer.

- Fix Fixed olivetti faces dataset `DESCR` attribute to point to the right location in `datasets.fetch_olivetti_faces`. #12441 by Jérémie du Boisberranger

- Fix `datasets.fetch_openml` to retry downloading when reading from local cache fails. #12517 by Thomas Fan.

### `sklearn.decomposition`

- Fix Fixed a regression in `decomposition.IncrementalPCA` where 0.20.0 raised an error if the number of samples in the final batch for fitting IncrementalPCA was smaller than n_components. #12234 by Ming Li.

### `sklearn.ensemble`

- Fix Fixed a bug mostly affecting `ensemble.RandomForestClassifier` where `class_weight='balanced_subsample'` failed with more than 32 classes. #12165 by Joel Nothman.

- Fix Fixed a bug affecting `ensemble.BaggingClassifier`, `ensemble.BaggingRegressor` and `ensemble.IsolationForest`, where `max_features` was sometimes rounded down to zero. #12388 by Connor Tann.

### `sklearn.feature_extraction`

- Fix Fixed a regression in v0.20.0 where `feature_extraction.text.CountVectorizer` and other text vectorizers could error during stop words validation with custom preprocessors or tokenizers. #12393 by Roman Yurchak.

### `sklearn.linear_model`

- Fix `linear_model.SGDClassifier` and variants with `early_stopping=True` would not use a consistent validation split in the multiclass case and this would cause a crash when using those estimators as part of parallel parameter search or cross-validation. #12122 by Olivier Grisel.

- Fix Fixed a bug affecting `linear_model.SGDClassifier` in the multiclass case. Each one-versus-all step is run in a `joblib.Parallel` call and mutating a common parameter, causing a segmentation fault if called within a backend using processes and not threads. We now use `require=sharedmem` at the `joblib.Parallel` instance creation. #12518 by Pierre Glaser and Olivier Grisel.

### `sklearn.metrics`

- Fix Fixed a bug in `metrics.pairwise.pairwise_distances_argmin_min` which returned the square root of the distance when the metric parameter was set to “euclidean”. #12481 by Jérémie du Boisberranger.

- Fix Fixed a bug in `metrics.pairwise.pairwise_distances_chunked` which didn’t ensure the diagonal is zero for euclidean distances. #12612 by Andreas Müller.

- API Change The `metrics.calinski_harabaz_score` has been renamed to `metrics.calinski_harabasz_score` and will be removed in version 0.23. #12211 by Lisa Thomas, Mark Hannel and Melissa Ferrari.

### `sklearn.mixture`

- Fix Ensure that the `fit_predict` method of `mixture.GaussianMixture` and `mixture.BayesianGaussianMixture` always yield assignments consistent with `fit` followed by `predict` even if the convergence criterion is too loose or not met. #12451 by Olivier Grisel.

### `sklearn.neighbors`

- Fix force the parallelism backend to `threading` for `neighbors.KDTree` and `neighbors.BallTree` in Python 2.7 to avoid pickling errors caused by the serialization of their methods. #12171 by Thomas Moreau.

### `sklearn.preprocessing`

- Fix Fixed bug in `preprocessing.OrdinalEncoder` when passing manually specified categories. #12365 by Joris Van den Bossche.

- Fix Fixed bug in `preprocessing.KBinsDiscretizer` where the `transform` method mutates the `_encoder` attribute. The `transform` method is now thread safe. #12514 by Hanmin Qin.

- Fix Fixed a bug in `preprocessing.PowerTransformer` where the Yeo-Johnson transform was incorrect for lambda parameters outside of `[0, 2]` #12522 by Nicolas Hug.

- Fix Fixed a bug in `preprocessing.OneHotEncoder` where transform failed when set to ignore unknown numpy strings of different lengths #12471 by Gabriel Marzinotto.

- API Change The default value of the `method` argument in `preprocessing.power_transform` will be changed from `box-cox` to `yeo-johnson` to match `preprocessing.PowerTransformer` in version 0.23. A FutureWarning is raised when the default value is used. #12317 by Eric Chang.

### `sklearn.utils`

- Fix Use float64 for mean accumulator to avoid floating point precision issues in `preprocessing.StandardScaler` and `decomposition.IncrementalPCA` when using float32 datasets. #12338 by bauks.

- Fix Calling `utils.check_array` on `pandas.Series`, which raised an error in 0.20.0, now returns the expected output again. #12625 by Andreas Müller

### Miscellaneous

- Fix When using site joblib by setting the environment variable `SKLEARN_SITE_JOBLIB`, added compatibility with joblib 0.11 in addition to 0.12+. #12350 by Joel Nothman and Roman Yurchak.

- Fix Make sure to avoid raising `FutureWarning` when calling `np.vstack` with numpy 1.16 and later (use list comprehensions instead of generator expressions in many locations of the scikit-learn code base). #12467 by Olivier Grisel.

- API Change Removed all mentions of `sklearn.externals.joblib`, and deprecated joblib methods exposed in `sklearn.utils`, except for `utils.parallel_backend` and `utils.register_parallel_backend`, which allow users to configure parallel computation in scikit-learn. Other functionalities are part of joblib. package and should be used directly, by installing it. The goal of this change is to prepare for unvendoring joblib in future version of scikit-learn. #12345 by Thomas Moreau

## Code and Documentation Contributors

With thanks to:

^__^, Adrin Jalali, Andrea Navarrete, Andreas Mueller, bauks, BenjaStudio, Cheuk Ting Ho, Connossor, Corey Levinson, Dan Stine, daten-kieker, Denis Kataev, Dillon Gardner, Dmitry Vukolov, Dougal J. Sutherland, Edward J Brown, Eric Chang, Federico Caselli, Gabriel Marzinotto, Gael Varoquaux, GauravAhlawat, Gustavo De Mari Pereira, Hanmin Qin, haroldfox, JackLangerman, Jacopo Notarstefano, janvanrijn, jdethurens, jeremiedbb, Joel Nothman, Joris Van den Bossche, Koen, Kushal Chauhan, Lee Yi Jie Joel, Lily Xiong, mail-liam, Mark Hannel, melsyt, Ming Li, Nicholas Smith, Nicolas Hug, Nikolay Shebanov, Oleksandr Pavlyk, Olivier Grisel, Peter Hausamann, Pierre Glaser, Pulkit Maloo, Quentin Batista, Radostin Stoyanov, Ramil Nugmanov, Rebekah Kim, Reshama Shaikh, Rohan Singh, Roman Feldbauer, Roman Yurchak, Roopam Sharma, Sam Waterbury, Scott Lowe, Sebastian Raschka, Stephen Tierney, SylvainLan, TakingItCasual, Thomas Fan, Thomas Moreau, Tom Dupré la Tour, Tulio Casagrande, Utkarsh Upadhyay, Xing Han Lu, Yaroslav Halchenko, Zach Miller

# Version 0.20.0

September 25, 2018

This release packs in a mountain of bug fixes, features and enhancements for the Scikit-learn library, and improvements to the documentation and examples. Thanks to our contributors!

This release is dedicated to the memory of Raghav Rajagopalan.

Warning

Version 0.20 is the last version of scikit-learn to support Python 2.7 and Python 3.4. Scikit-learn 0.21 will require Python 3.5 or higher.

## Highlights

We have tried to improve our support for common data-science use-cases including missing values, categorical variables, heterogeneous data, and features/targets with unusual distributions. Missing values in features, represented by NaNs, are now accepted in column-wise preprocessing such as scalers. Each feature is fitted disregarding NaNs, and data containing NaNs can be transformed. The new `sklearn.impute` module provides estimators for learning despite missing data.

`ColumnTransformer` handles the case where different features or columns of a pandas.DataFrame need different preprocessing. String or pandas Categorical columns can now be encoded with `OneHotEncoder` or `OrdinalEncoder`.

`TransformedTargetRegressor` helps when the regression target needs to be transformed to be modeled. `PowerTransformer` and `KBinsDiscretizer` join `QuantileTransformer` as non-linear transformations.

Beyond this, we have added sample_weight support to several estimators (including `KMeans`, `BayesianRidge` and `KernelDensity`) and improved stopping criteria in others (including `MLPRegressor`, `GradientBoostingRegressor` and `SGDRegressor`).

This release is also the first to be accompanied by a Glossary of Common Terms and API Elements developed by Joel Nothman. The glossary is a reference resource to help users and contributors become familiar with the terminology and conventions used in Scikit-learn.

Sorry if your contribution didn’t make it into the highlights. There’s a lot here…

## 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.

- `cluster.MeanShift` (bug fix)

- `decomposition.IncrementalPCA` in Python 2 (bug fix)

- `decomposition.SparsePCA` (bug fix)

- `ensemble.GradientBoostingClassifier` (bug fix affecting feature importances)

- `isotonic.IsotonicRegression` (bug fix)

- `linear_model.ARDRegression` (bug fix)

- `linear_model.LogisticRegressionCV` (bug fix)

- `linear_model.OrthogonalMatchingPursuit` (bug fix)

- `linear_model.PassiveAggressiveClassifier` (bug fix)

- `linear_model.PassiveAggressiveRegressor` (bug fix)

- `linear_model.Perceptron` (bug fix)

- `linear_model.SGDClassifier` (bug fix)

- `linear_model.SGDRegressor` (bug fix)

- `metrics.roc_auc_score` (bug fix)

- `metrics.roc_curve` (bug fix)

- `neural_network.BaseMultilayerPerceptron` (bug fix)

- `neural_network.MLPClassifier` (bug fix)

- `neural_network.MLPRegressor` (bug fix)

- The v0.19.0 release notes failed to mention a backwards incompatibility with `model_selection.StratifiedKFold` when `shuffle=True` due to #7823.

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

- #11924: `linear_model.LogisticRegressionCV` with `solver='lbfgs'` and `multi_class='multinomial'` may be non-deterministic or otherwise broken on macOS. This appears to be the case on Travis CI servers, but has not been confirmed on personal MacBooks! This issue has been present in previous releases.

- #9354: `metrics.pairwise.euclidean_distances` (which is used several times throughout the library) gives results with poor precision, which particularly affects its use with 32-bit float inputs. This became more problematic in versions 0.18 and 0.19 when some algorithms were changed to avoid casting 32-bit data into 64-bit.

## Changelog

Support for Python 3.3 has been officially dropped.

### `sklearn.cluster`

- Major Feature `cluster.AgglomerativeClustering` now supports Single Linkage clustering via `linkage='single'`. #9372 by Leland McInnes and Steve Astels.

- Feature `cluster.KMeans` and `cluster.MiniBatchKMeans` now support sample weights via new parameter `sample_weight` in `fit` function. #10933 by Johannes Hansen.

- Efficiency `cluster.KMeans`, `cluster.MiniBatchKMeans` and `cluster.k_means` passed with `algorithm='full'` now enforces row-major ordering, improving runtime. #10471 by Gaurav Dhingra.

- Efficiency `cluster.DBSCAN` now is parallelized according to `n_jobs` regardless of `algorithm`. #8003 by Joël Billaud.

- Enhancement `cluster.KMeans` now gives a warning if the number of distinct clusters found is smaller than `n_clusters`. This may occur when the number of distinct points in the data set is actually smaller than the number of cluster one is looking for. #10059 by Christian Braune.

- Fix Fixed a bug where the `fit` method of `cluster.AffinityPropagation` stored cluster centers as 3d array instead of 2d array in case of non-convergence. For the same class, fixed undefined and arbitrary behavior in case of training data where all samples had equal similarity. #9612. By Jonatan Samoocha.

- Fix Fixed a bug in `cluster.spectral_clustering` where the normalization of the spectrum was using a division instead of a multiplication. #8129 by Jan Margeta, Guillaume Lemaitre, and Devansh D..

- Fix Fixed a bug in `cluster.k_means_elkan` where the returned `iteration` was 1 less than the correct value. Also added the missing `n_iter_` attribute in the docstring of `cluster.KMeans`. #11353 by Jeremie du Boisberranger.

- Fix Fixed a bug in `cluster.mean_shift` where the assigned labels were not deterministic if there were multiple clusters with the same intensities. #11901 by Adrin Jalali.

- API Change Deprecate `pooling_func` unused parameter in `cluster.AgglomerativeClustering`. #9875 by Kumar Ashutosh.

### `sklearn.compose`

- New module.

- Major Feature Added `compose.ColumnTransformer`, which allows to apply different transformers to different columns of arrays or pandas DataFrames. #9012 by Andreas Müller and Joris Van den Bossche, and #11315 by Thomas Fan.

- Major Feature Added the `compose.TransformedTargetRegressor` which transforms the target y before fitting a regression model. The predictions are mapped back to the original space via an inverse transform. #9041 by Andreas Müller and Guillaume Lemaitre.

### `sklearn.covariance`

- Efficiency Runtime improvements to `covariance.GraphicalLasso`. #9858 by Steven Brown.

- API Change The `covariance.graph_lasso`, `covariance.GraphLasso` and `covariance.GraphLassoCV` have been renamed to `covariance.graphical_lasso`, `covariance.GraphicalLasso` and `covariance.GraphicalLassoCV` respectively and will be removed in version 0.22. #9993 by Artiem Krinitsyn

### `sklearn.datasets`

- Major Feature Added `datasets.fetch_openml` to fetch datasets from OpenML. OpenML is a free, open data sharing platform and will be used instead of mldata as it provides better service availability. #9908 by Andreas Müller and Jan N. van Rijn.

- Feature In `datasets.make_blobs`, one can now pass a list to the `n_samples` parameter to indicate the number of samples to generate per cluster. #8617 by Maskani Filali Mohamed and Konstantinos Katrioplas.

- Feature Add `filename` attribute to `sklearn.datasets` that have a CSV file. #9101 by alex-33 and Maskani Filali Mohamed.

- Feature `return_X_y` parameter has been added to several dataset loaders. #10774 by Chris Catalfo.

- Fix Fixed a bug in `datasets.load_boston` which had a wrong data point. #10795 by Takeshi Yoshizawa.

- Fix Fixed a bug in `datasets.load_iris` which had two wrong data points. #11082 by Sadhana Srinivasan and Hanmin Qin.

- Fix Fixed a bug in `datasets.fetch_kddcup99`, where data were not properly shuffled. #9731 by Nicolas Goix.

- Fix Fixed a bug in `datasets.make_circles`, where no odd number of data points could be generated. #10045 by Christian Braune.

- API Change Deprecated `sklearn.datasets.fetch_mldata` to be removed in version 0.22. mldata.org is no longer operational. Until removal it will remain possible to load cached datasets. #11466 by Joel Nothman.

### `sklearn.decomposition`

- Feature `decomposition.dict_learning` functions and models now support positivity constraints. This applies to the dictionary and sparse code. #6374 by John Kirkham.

- Feature Fix `decomposition.SparsePCA` now exposes `normalize_components`. When set to True, the train and test data are centered with the train mean respectively during the fit phase and the transform phase. This fixes the behavior of SparsePCA. When set to False, which is the default, the previous abnormal behaviour still holds. The False value is for backward compatibility and should not be used. #11585 by Ivan Panico.

- Efficiency Efficiency improvements in `decomposition.dict_learning`. #11420 and others by John Kirkham.

- Fix Fix for uninformative error in `decomposition.IncrementalPCA`: now an error is raised if the number of components is larger than the chosen batch size. The `n_components=None` case was adapted accordingly. #6452. By Wally Gauze.

- Fix Fixed a bug where the `partial_fit` method of `decomposition.IncrementalPCA` used integer division instead of float division on Python 2. #9492 by James Bourbeau.

- Fix In `decomposition.PCA` selecting a n_components parameter greater than the number of samples now raises an error. Similarly, the `n_components=None` case now selects the minimum of `n_samples` and `n_features`. #8484 by Wally Gauze.

- Fix Fixed a bug in `decomposition.PCA` where users will get unexpected error with large datasets when `n_components='mle'` on Python 3 versions. #9886 by Hanmin Qin.

- Fix Fixed an underflow in calculating KL-divergence for `decomposition.NMF` #10142 by Tom Dupre la Tour.

- Fix Fixed a bug in `decomposition.SparseCoder` when running OMP sparse coding in parallel using read-only memory mapped datastructures. #5956 by Vighnesh Birodkar and Olivier Grisel.

### `sklearn.discriminant_analysis`

- Efficiency Memory usage improvement for `_class_means` and `_class_cov` in `sklearn.discriminant_analysis`. #10898 by Nanxin Chen.

### `sklearn.dummy`

- Feature `dummy.DummyRegressor` now has a `return_std` option in its `predict` method. The returned standard deviations will be zeros.

- Feature `dummy.DummyClassifier` and `dummy.DummyRegressor` now only require X to be an object with finite length or shape. #9832 by Vrishank Bhardwaj.

- Feature `dummy.DummyClassifier` and `dummy.DummyRegressor` can now be scored without supplying test samples. #11951 by Rüdiger Busche.

### `sklearn.ensemble`

- Feature `ensemble.BaggingRegressor` and `ensemble.BaggingClassifier` can now be fit with missing/non-finite values in X and/or multi-output Y to support wrapping pipelines that perform their own imputation. #9707 by Jimmy Wan.

- Feature `ensemble.GradientBoostingClassifier` and `ensemble.GradientBoostingRegressor` now support early stopping via `n_iter_no_change`, `validation_fraction` and `tol`. #7071 by Raghav RV

- Feature Added `named_estimators_` parameter in `ensemble.VotingClassifier` to access fitted estimators. #9157 by Herilalaina Rakotoarison.

- Fix Fixed a bug when fitting `ensemble.GradientBoostingClassifier` or `ensemble.GradientBoostingRegressor` with `warm_start=True` which previously raised a segmentation fault due to a non-conversion of CSC matrix into CSR format expected by `decision_function`. Similarly, Fortran-ordered arrays are converted to C-ordered arrays in the dense case. #9991 by Guillaume Lemaitre.

- Fix Fixed a bug in `ensemble.GradientBoostingRegressor` and `ensemble.GradientBoostingClassifier` to have feature importances summed and then normalized, rather than normalizing on a per-tree basis. The previous behavior over-weighted the Gini importance of features that appear in later stages. This issue only affected feature importances. #11176 by Gil Forsyth.

- API Change The default value of the `n_estimators` parameter of `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor`, `ensemble.ExtraTreesClassifier`, `ensemble.ExtraTreesRegressor`, and `ensemble.RandomTreesEmbedding` will change from 10 in version 0.20 to 100 in 0.22. A FutureWarning is raised when the default value is used. #11542 by Anna Ayzenshtat.

- API Change Classes derived from `ensemble.BaseBagging`. The attribute `estimators_samples_` will return a list of arrays containing the indices selected for each bootstrap instead of a list of arrays containing the mask of the samples selected for each bootstrap. Indices allows to repeat samples while mask does not allow this functionality. #9524 by Guillaume Lemaitre.

- Fix `ensemble.BaseBagging` where one could not deterministically reproduce `fit` result using the object attributes when `random_state` is set. #9723 by Guillaume Lemaitre.

### `sklearn.feature_extraction`

- Feature Enable the call to `get_feature_names` in unfitted `feature_extraction.text.CountVectorizer` initialized with a vocabulary. #10908 by Mohamed Maskani.

- Enhancement `idf_` can now be set on a `feature_extraction.text.TfidfTransformer`. #10899 by Sergey Melderis.

- Fix Fixed a bug in `feature_extraction.image.extract_patches_2d` which would throw an exception if `max_patches` was greater than or equal to the number of all possible patches rather than simply returning the number of possible patches. #10101 by Varun Agrawal

- Fix Fixed a bug in `feature_extraction.text.CountVectorizer`, `feature_extraction.text.TfidfVectorizer`, `feature_extraction.text.HashingVectorizer` to support 64 bit sparse array indexing necessary to process large datasets with more than 2·10⁹ tokens (words or n-grams). #9147 by Claes-Fredrik Mannby and Roman Yurchak.

- Fix Fixed bug in `feature_extraction.text.TfidfVectorizer` which was ignoring the parameter `dtype`. In addition, `feature_extraction.text.TfidfTransformer` will preserve `dtype` for floating and raise a warning if `dtype` requested is integer. #10441 by Mayur Kulkarni and Guillaume Lemaitre.

### `sklearn.feature_selection`

- Feature Added select K best features functionality to `feature_selection.SelectFromModel`. #6689 by Nihar Sheth and Quazi Rahman.

- Feature Added `min_features_to_select` parameter to `feature_selection.RFECV` to bound evaluated features counts. #11293 by Brent Yi.

- Feature `feature_selection.RFECV`’s fit method now supports groups. #9656 by Adam Greenhall.

- Fix Fixed computation of `n_features_to_compute` for edge case with tied CV scores in `feature_selection.RFECV`. #9222 by Nick Hoh.

### `sklearn.gaussian_process`

- Efficiency In `gaussian_process.GaussianProcessRegressor`, method `predict` is faster when using `return_std=True` in particular more when called several times in a row. #9234 by andrewww and Minghui Liu.

### `sklearn.impute`

- New module, adopting `preprocessing.Imputer` as `impute.SimpleImputer` with minor changes (see under preprocessing below).

- Major Feature Added `impute.MissingIndicator` which generates a binary indicator for missing values. #8075 by Maniteja Nandana and Guillaume Lemaitre.

- Feature The `impute.SimpleImputer` has a new strategy, `'constant'`, to complete missing values with a fixed one, given by the `fill_value` parameter. This strategy supports numeric and non-numeric data, and so does the `'most_frequent'` strategy now. #11211 by Jeremie du Boisberranger.

### `sklearn.isotonic`

- Fix Fixed a bug in `isotonic.IsotonicRegression` which incorrectly combined weights when fitting a model to data involving points with identical X values. #9484 by Dallas Card

### `sklearn.linear_model`

- Feature `linear_model.SGDClassifier`, `linear_model.SGDRegressor`, `linear_model.PassiveAggressiveClassifier`, `linear_model.PassiveAggressiveRegressor` and `linear_model.Perceptron` now expose `early_stopping`, `validation_fraction` and `n_iter_no_change` parameters, to stop optimization monitoring the score on a validation set. A new learning rate `"adaptive"` strategy divides the learning rate by 5 each time `n_iter_no_change` consecutive epochs fail to improve the model. #9043 by Tom Dupre la Tour.

- Feature Add `sample_weight` parameter to the fit method of `linear_model.BayesianRidge` for weighted linear regression. #10112 by Peter St. John.

- Fix Fixed a bug in `logistic.logistic_regression_path` to ensure that the returned coefficients are correct when `multiclass='multinomial'`. Previously, some of the coefficients would override each other, leading to incorrect results in `linear_model.LogisticRegressionCV`. #11724 by Nicolas Hug.

- Fix Fixed a bug in `linear_model.LogisticRegression` where when using the parameter `multi_class='multinomial'`, the `predict_proba` method was returning incorrect probabilities in the case of binary outcomes. #9939 by Roger Westover.

- Fix Fixed a bug in `linear_model.LogisticRegressionCV` where the `score` method always computes accuracy, not the metric given by the `scoring` parameter. #10998 by Thomas Fan.

- Fix Fixed a bug in `linear_model.LogisticRegressionCV` where the ‘ovr’ strategy was always used to compute cross-validation scores in the multiclass setting, even if `'multinomial'` was set. #8720 by William de Vazelhes.

- Fix Fixed a bug in `linear_model.OrthogonalMatchingPursuit` that was broken when setting `normalize=False`. #10071 by Alexandre Gramfort.

- Fix Fixed a bug in `linear_model.ARDRegression` which caused incorrectly updated estimates for the standard deviation and the coefficients. #10153 by Jörg Döpfert.

- Fix Fixed a bug in `linear_model.ARDRegression` and `linear_model.BayesianRidge` which caused NaN predictions when fitted with a constant target. #10095 by Jörg Döpfert.

- Fix Fixed a bug in `linear_model.RidgeClassifierCV` where the parameter `store_cv_values` was not implemented though it was documented in `cv_values` as a way to set up the storage of cross-validation values for different alphas. #10297 by Mabel Villalba-Jiménez.

- Fix Fixed a bug in `linear_model.ElasticNet` which caused the input to be overridden when using parameter `copy_X=True` and `check_input=False`. #10581 by Yacine Mazari.

- Fix Fixed a bug in `sklearn.linear_model.Lasso` where the coefficient had wrong shape when `fit_intercept=False`. #10687 by Martin Hahn.

- Fix Fixed a bug in `sklearn.linear_model.LogisticRegression` where the `multi_class='multinomial'` with binary output `with warm_start=True` #10836 by Aishwarya Srinivasan.

- Fix Fixed a bug in `linear_model.RidgeCV` where using integer `alphas` raised an error. #10397 by Mabel Villalba-Jiménez.

- Fix Fixed condition triggering gap computation in `linear_model.Lasso` and `linear_model.ElasticNet` when working with sparse matrices. #10992 by Alexandre Gramfort.

- Fix Fixed a bug in `linear_model.SGDClassifier`, `linear_model.SGDRegressor`, `linear_model.PassiveAggressiveClassifier`, `linear_model.PassiveAggressiveRegressor` and `linear_model.Perceptron`, where the stopping criterion was stopping the algorithm before convergence. A parameter `n_iter_no_change` was added and set by default to 5. Previous behavior is equivalent to setting the parameter to 1. #9043 by Tom Dupre la Tour.

- Fix Fixed a bug where liblinear and libsvm-based estimators would segfault if passed a scipy.sparse matrix with 64-bit indices. They now raise a ValueError. #11327 by Karan Dhingra and Joel Nothman.

- API Change The default values of the `solver` and `multi_class` parameters of `linear_model.LogisticRegression` will change respectively from `'liblinear'` and `'ovr'` in version 0.20 to `'lbfgs'` and `'auto'` in version 0.22. A FutureWarning is raised when the default values are used. #11905 by Tom Dupre la Tour and Joel Nothman.

- API Change Deprecate `positive=True` option in `linear_model.Lars` as the underlying implementation is broken. Use `linear_model.Lasso` instead. #9837 by Alexandre Gramfort.

- API Change `n_iter_` may vary from previous releases in `linear_model.LogisticRegression` with `solver='lbfgs'` and `linear_model.HuberRegressor`. For Scipy <= 1.0.0, the optimizer could perform more than the requested maximum number of iterations. Now both estimators will report at most `max_iter` iterations even if more were performed. #10723 by Joel Nothman.

### `sklearn.manifold`

- Efficiency Speed improvements for both ‘exact’ and ‘barnes_hut’ methods in `manifold.TSNE`. #10593 and #10610 by Tom Dupre la Tour.

- Feature Support sparse input in `manifold.Isomap.fit`. #8554 by Leland McInnes.

- Feature `manifold.t_sne.trustworthiness` accepts metrics other than Euclidean. #9775 by William de Vazelhes.

- Fix Fixed a bug in `manifold.spectral_embedding` where the normalization of the spectrum was using a division instead of a multiplication. #8129 by Jan Margeta, Guillaume Lemaitre, and Devansh D..

- API Change Feature Deprecate `precomputed` parameter in function `manifold.t_sne.trustworthiness`. Instead, the new parameter `metric` should be used with any compatible metric including ‘precomputed’, in which case the input matrix `X` should be a matrix of pairwise distances or squared distances. #9775 by William de Vazelhes.

- API Change Deprecate `precomputed` parameter in function `manifold.t_sne.trustworthiness`. Instead, the new parameter `metric` should be used with any compatible metric including ‘precomputed’, in which case the input matrix `X` should be a matrix of pairwise distances or squared distances. #9775 by William de Vazelhes.

### `sklearn.metrics`

- Major Feature Added the `metrics.davies_bouldin_score` metric for evaluation of clustering models without a ground truth. #10827 by Luis Osa.

- Major Feature Added the `metrics.balanced_accuracy_score` metric and a corresponding `'balanced_accuracy'` scorer for binary and multiclass classification. #8066 by @xyguo and Aman Dalmia, and #10587 by Joel Nothman.

- Feature Partial AUC is available via `max_fpr` parameter in `metrics.roc_auc_score`. #3840 by Alexander Niederbühl.

- Feature A scorer based on `metrics.brier_score_loss` is also available. #9521 by Hanmin Qin.

- Feature Added control over the normalization in `metrics.normalized_mutual_info_score` and `metrics.adjusted_mutual_info_score` via the `average_method` parameter. In version 0.22, the default normalizer for each will become the arithmetic mean of the entropies of each clustering. #11124 by Arya McCarthy.

- Feature Added `output_dict` parameter in `metrics.classification_report` to return classification statistics as dictionary. #11160 by Dan Barkhorn.

- Feature `metrics.classification_report` now reports all applicable averages on the given data, including micro, macro and weighted average as well as samples average for multilabel data. #11679 by Alexander Pacha.

- Feature `metrics.average_precision_score` now supports binary `y_true` other than `{0, 1}` or `{-1, 1}` through `pos_label` parameter. #9980 by Hanmin Qin.

- Feature `metrics.label_ranking_average_precision_score` now supports `sample_weight`. #10845 by Jose Perez-Parras Toledano.

- Feature Add `dense_output` parameter to `metrics.pairwise.linear_kernel`. When False and both inputs are sparse, will return a sparse matrix. #10999 by Taylor G Smith.

- Efficiency `metrics.silhouette_score` and `metrics.silhouette_samples` are more memory efficient and run faster. This avoids some reported freezes and MemoryErrors. #11135 by Joel Nothman.

- Fix Fixed a bug in `metrics.precision_recall_fscore_support` when truncated `range(n_labels)` is passed as value for `labels`. #10377 by Gaurav Dhingra.

- Fix Fixed a bug due to floating point error in `metrics.roc_auc_score` with non-integer sample weights. #9786 by Hanmin Qin.

- Fix Fixed a bug where `metrics.roc_curve` sometimes starts on y-axis instead of (0, 0), which is inconsistent with the document and other implementations. Note that this will not influence the result from `metrics.roc_auc_score` #10093 by alexryndin and Hanmin Qin.

- Fix Fixed a bug to avoid integer overflow. Casted product to 64 bits integer in `metrics.mutual_info_score`. #9772 by Kumar Ashutosh.

- Fix Fixed a bug where `metrics.average_precision_score` will sometimes return `nan` when `sample_weight` contains 0. #9980 by Hanmin Qin.

- Fix Fixed a bug in `metrics.fowlkes_mallows_score` to avoid integer overflow. Casted return value of `contingency_matrix` to `int64` and computed product of square roots rather than square root of product. #9515 by Alan Liddell and Manh Dao.

- API Change Deprecate `reorder` parameter in `metrics.auc` as it’s no longer required for `metrics.roc_auc_score`. Moreover using `reorder=True` can hide bugs due to floating point error in the input. #9851 by Hanmin Qin.

- API Change In `metrics.normalized_mutual_info_score` and `metrics.adjusted_mutual_info_score`, warn that `average_method` will have a new default value. In version 0.22, the default normalizer for each will become the arithmetic mean of the entropies of each clustering. Currently, `metrics.normalized_mutual_info_score` uses the default of `average_method='geometric'`, and `metrics.adjusted_mutual_info_score` uses the default of `average_method='max'` to match their behaviors in version 0.19. #11124 by Arya McCarthy.

- API Change The `batch_size` parameter to `metrics.pairwise_distances_argmin_min` and `metrics.pairwise_distances_argmin` is deprecated to be removed in v0.22. It no longer has any effect, as batch size is determined by global `working_memory` config. See Limiting Working Memory. #10280 by Joel Nothman and Aman Dalmia.

### `sklearn.mixture`

- Feature Added function fit_predict to `mixture.GaussianMixture` and `mixture.GaussianMixture`, which is essentially equivalent to calling fit and predict. #10336 by Shu Haoran and Andrew Peng.

- Fix Fixed a bug in `mixture.BaseMixture` where the reported `n_iter_` was missing an iteration. It affected `mixture.GaussianMixture` and `mixture.BayesianGaussianMixture`. #10740 by Erich Schubert and Guillaume Lemaitre.

- Fix Fixed a bug in `mixture.BaseMixture` and its subclasses `mixture.GaussianMixture` and `mixture.BayesianGaussianMixture` where the `lower_bound_` was not the max lower bound across all initializations (when `n_init > 1`), but just the lower bound of the last initialization. #10869 by Aurélien Géron.

### `sklearn.model_selection`

- Feature Add `return_estimator` parameter in `model_selection.cross_validate` to return estimators fitted on each split. #9686 by Aurélien Bellet.

- Feature New `refit_time_` attribute will be stored in `model_selection.GridSearchCV` and `model_selection.RandomizedSearchCV` if `refit` is set to `True`. This will allow measuring the complete time it takes to perform hyperparameter optimization and refitting the best model on the whole dataset. #11310 by Matthias Feurer.

- Feature Expose `error_score` parameter in `model_selection.cross_validate`, `model_selection.cross_val_score`, `model_selection.learning_curve` and `model_selection.validation_curve` to control the behavior triggered when an error occurs in `model_selection._fit_and_score`. #11576 by Samuel O. Ronsin.

- Feature `BaseSearchCV` now has an experimental, private interface to support customized parameter search strategies, through its `_run_search` method. See the implementations in `model_selection.GridSearchCV` and `model_selection.RandomizedSearchCV` and please provide feedback if you use this. Note that we do not assure the stability of this API beyond version 0.20. #9599 by Joel Nothman

- Enhancement Add improved error message in `model_selection.cross_val_score` when multiple metrics are passed in `scoring` keyword. #11006 by Ming Li.

- API Change The default number of cross-validation folds `cv` and the default number of splits `n_splits` in the `model_selection.KFold`-like splitters will change from 3 to 5 in 0.22 as 3-fold has a lot of variance. #11557 by Alexandre Boucaud.

- API Change The default of `iid` parameter of `model_selection.GridSearchCV` and `model_selection.RandomizedSearchCV` will change from `True` to `False` in version 0.22 to correspond to the standard definition of cross-validation, and the parameter will be removed in version 0.24 altogether. This parameter is of greatest practical significance where the sizes of different test sets in cross-validation were very unequal, i.e. in group-based CV strategies. #9085 by Laurent Direr and Andreas Müller.

- API Change The default value of the `error_score` parameter in `model_selection.GridSearchCV` and `model_selection.RandomizedSearchCV` will change to `np.NaN` in version 0.22. #10677 by Kirill Zhdanovich.

- API Change Changed ValueError exception raised in `model_selection.ParameterSampler` to a UserWarning for case where the class is instantiated with a greater value of `n_iter` than the total space of parameters in the parameter grid. `n_iter` now acts as an upper bound on iterations. #10982 by Juliet Lawton

- API Change Invalid input for `model_selection.ParameterGrid` now raises TypeError. #10928 by Solutus Immensus

### `sklearn.multioutput`

- Major Feature Added `multioutput.RegressorChain` for multi-target regression. #9257 by Kumar Ashutosh.

### `sklearn.naive_bayes`

- Major Feature Added `naive_bayes.ComplementNB`, which implements the Complement Naive Bayes classifier described in Rennie et al. (2003). #8190 by Michael A. Alcorn.

- Feature Add `var_smoothing` parameter in `naive_bayes.GaussianNB` to give a precise control over variances calculation. #9681 by Dmitry Mottl.

- Fix Fixed a bug in `naive_bayes.GaussianNB` which incorrectly raised error for prior list which summed to 1. #10005 by Gaurav Dhingra.

- Fix Fixed a bug in `naive_bayes.MultinomialNB` which did not accept vector valued pseudocounts (alpha). #10346 by Tobias Madsen

### `sklearn.neighbors`

- Efficiency `neighbors.RadiusNeighborsRegressor` and `neighbors.RadiusNeighborsClassifier` are now parallelized according to `n_jobs` regardless of `algorithm`. #10887 by Joël Billaud.

- Efficiency `sklearn.neighbors` query methods are now more memory efficient when `algorithm='brute'`. #11136 by Joel Nothman and Aman Dalmia.

- Feature Add `sample_weight` parameter to the fit method of `neighbors.KernelDensity` to enable weighting in kernel density estimation. #4394 by Samuel O. Ronsin.

- Feature Novelty detection with `neighbors.LocalOutlierFactor`: Add a `novelty` parameter to `neighbors.LocalOutlierFactor`. When `novelty` is set to True, `neighbors.LocalOutlierFactor` can then be used for novelty detection, i.e. predict on new unseen data. Available prediction methods are `predict`, `decision_function` and `score_samples`. By default, `novelty` is set to `False`, and only the `fit_predict` method is available. By Albert Thomas.

- Fix Fixed a bug in `neighbors.NearestNeighbors` where fitting a NearestNeighbors model fails when a) the distance metric used is a callable and b) the input to the NearestNeighbors model is sparse. #9579 by Thomas Kober.

- Fix Fixed a bug so `predict` in `neighbors.RadiusNeighborsRegressor` can handle empty neighbor set when using non uniform weights. Also raises a new warning when no neighbors are found for samples. #9655 by Andreas Bjerre-Nielsen.

- Fix Efficiency Fixed a bug in `KDTree` construction that results in faster construction and querying times. #11556 by Jake VanderPlas

- Fix Fixed a bug in `neighbors.KDTree` and `neighbors.BallTree` where pickled tree objects would change their type to the super class `BinaryTree`. #11774 by Nicolas Hug.

### `sklearn.neural_network`

- Feature Add `n_iter_no_change` parameter in `neural_network.BaseMultilayerPerceptron`, `neural_network.MLPRegressor`, and `neural_network.MLPClassifier` to give control over maximum number of epochs to not meet `tol` improvement. #9456 by Nicholas Nadeau.

- Fix Fixed a bug in `neural_network.BaseMultilayerPerceptron`, `neural_network.MLPRegressor`, and `neural_network.MLPClassifier` with new `n_iter_no_change` parameter now at 10 from previously hardcoded 2. #9456 by Nicholas Nadeau.

- Fix Fixed a bug in `neural_network.MLPRegressor` where fitting quit unexpectedly early due to local minima or fluctuations. #9456 by Nicholas Nadeau

### `sklearn.pipeline`

- Feature The `predict` method of `pipeline.Pipeline` now passes keyword arguments on to the pipeline’s last estimator, enabling the use of parameters such as `return_std` in a pipeline with caution. #9304 by Breno Freitas.

- API Change `pipeline.FeatureUnion` now supports `'drop'` as a transformer to drop features. #11144 by Thomas Fan.

### `sklearn.preprocessing`

- Major Feature Expanded `preprocessing.OneHotEncoder` to allow to encode categorical string features as a numeric array using a one-hot (or dummy) encoding scheme, and added `preprocessing.OrdinalEncoder` to convert to ordinal integers. Those two classes now handle encoding of all feature types (also handles string-valued features) and derives the categories based on the unique values in the features instead of the maximum value in the features. #9151 and #10521 by Vighnesh Birodkar and Joris Van den Bossche.

- Major Feature Added `preprocessing.KBinsDiscretizer` for turning continuous features into categorical or one-hot encoded features. #7668, #9647, #10195, #10192, #11272, #11467 and #11505. by Henry Lin, Hanmin Qin, Tom Dupre la Tour and Giovanni Giuseppe Costa.

- Major Feature Added `preprocessing.PowerTransformer`, which implements the Yeo-Johnson and Box-Cox power transformations. Power transformations try to find a set of feature-wise parametric transformations to approximately map data to a Gaussian distribution centered at zero and with unit variance. This is useful as a variance-stabilizing transformation in situations where normality and homoscedasticity are desirable. #10210 by Eric Chang and Maniteja Nandana, and #11520 by Nicolas Hug.

- Major Feature NaN values are ignored and handled in the following preprocessing methods: `preprocessing.MaxAbsScaler`, `preprocessing.MinMaxScaler`, `preprocessing.RobustScaler`, `preprocessing.StandardScaler`, `preprocessing.PowerTransformer`, `preprocessing.QuantileTransformer` classes and `preprocessing.maxabs_scale`, `preprocessing.minmax_scale`, `preprocessing.robust_scale`, `preprocessing.scale`, `preprocessing.power_transform`, `preprocessing.quantile_transform` functions respectively addressed in issues #11011, #11005, #11308, #11206, #11306, and #10437. By Lucija Gregov and Guillaume Lemaitre.

- Feature `preprocessing.PolynomialFeatures` now supports sparse input. #10452 by Aman Dalmia and Joel Nothman.

- Feature `preprocessing.RobustScaler` and `preprocessing.robust_scale` can be fitted using sparse matrices. #11308 by Guillaume Lemaitre.

- Feature `preprocessing.OneHotEncoder` now supports the `get_feature_names` method to obtain the transformed feature names. #10181 by Nirvan Anjirbag and Joris Van den Bossche.

- Feature A parameter `check_inverse` was added to `preprocessing.FunctionTransformer` to ensure that `func` and `inverse_func` are the inverse of each other. #9399 by Guillaume Lemaitre.

- Feature The `transform` method of `sklearn.preprocessing.MultiLabelBinarizer` now ignores any unknown classes. A warning is raised stating the unknown classes classes found which are ignored. #10913 by Rodrigo Agundez.

- Fix Fixed bugs in `preprocessing.LabelEncoder` which would sometimes throw errors when `transform` or `inverse_transform` was called with empty arrays. #10458 by Mayur Kulkarni.

- Fix Fix ValueError in `preprocessing.LabelEncoder` when using `inverse_transform` on unseen labels. #9816 by Charlie Newey.

- Fix Fix bug in `preprocessing.OneHotEncoder` which discarded the `dtype` when returning a sparse matrix output. #11042 by Daniel Morales.

- Fix Fix `fit` and `partial_fit` in `preprocessing.StandardScaler` in the rare case when `with_mean=False` and `with_std=False` which was crashing by calling `fit` more than once and giving inconsistent results for `mean_` whether the input was a sparse or a dense matrix. `mean_` will be set to `None` with both sparse and dense inputs. `n_samples_seen_` will be also reported for both input types. #11235 by Guillaume Lemaitre.

- API Change Deprecate `n_values` and `categorical_features` parameters and `active_features_`, `feature_indices_` and `n_values_` attributes of `preprocessing.OneHotEncoder`. The `n_values` parameter can be replaced with the new `categories` parameter, and the attributes with the new `categories_` attribute. Selecting the categorical features with the `categorical_features` parameter is now better supported using the `compose.ColumnTransformer`. #10521 by Joris Van den Bossche.

- API Change Deprecate `preprocessing.Imputer` and move the corresponding module to `impute.SimpleImputer`. #9726 by Kumar Ashutosh.

- API Change The `axis` parameter that was in `preprocessing.Imputer` is no longer present in `impute.SimpleImputer`. The behavior is equivalent to `axis=0` (impute along columns). Row-wise imputation can be performed with FunctionTransformer (e.g., `FunctionTransformer(lambda X: SimpleImputer().fit_transform(X.T).T)`). #10829 by Guillaume Lemaitre and Gilberto Olimpio.

- API Change The NaN marker for the missing values has been changed between the `preprocessing.Imputer` and the `impute.SimpleImputer`. `missing_values='NaN'` should now be `missing_values=np.nan`. #11211 by Jeremie du Boisberranger.

- API Change In `preprocessing.FunctionTransformer`, the default of `validate` will be from `True` to `False` in 0.22. #10655 by Guillaume Lemaitre.

### `sklearn.svm`

- Fix Fixed a bug in `svm.SVC` where when the argument `kernel` is unicode in Python2, the `predict_proba` method was raising an unexpected TypeError given dense inputs. #10412 by Jiongyan Zhang.

- API Change Deprecate `random_state` parameter in `svm.OneClassSVM` as the underlying implementation is not random. #9497 by Albert Thomas.

- API Change The default value of `gamma` parameter of `svm.SVC`, `NuSVC`, `SVR`, `NuSVR`, `OneClassSVM` will change from `'auto'` to `'scale'` in version 0.22 to account better for unscaled features. #8361 by Gaurav Dhingra and Ting Neo.

### `sklearn.tree`

- Enhancement Although private (and hence not assured API stability), `tree._criterion.ClassificationCriterion` and `tree._criterion.RegressionCriterion` may now be cimported and extended. #10325 by Camil Staps.

- Fix Fixed a bug in `tree.BaseDecisionTree` with `splitter="best"` where split threshold could become infinite when values in X were near infinite. #10536 by Jonathan Ohayon.

- Fix Fixed a bug in `tree.MAE` to ensure sample weights are being used during the calculation of tree MAE impurity. Previous behaviour could cause suboptimal splits to be chosen since the impurity calculation considered all samples to be of equal weight importance. #11464 by John Stott.

### `sklearn.utils`

- Feature `utils.check_array` and `utils.check_X_y` now have `accept_large_sparse` to control whether scipy.sparse matrices with 64-bit indices should be rejected. #11327 by Karan Dhingra and Joel Nothman.

- Efficiency Fix Avoid copying the data in `utils.check_array` when the input data is a memmap (and `copy=False`). #10663 by Arthur Mensch and Loïc Estève.

- API Change `utils.check_array` yield a `FutureWarning` indicating that arrays of bytes/strings will be interpreted as decimal numbers beginning in version 0.22. #10229 by Ryan Lee

### Multiple modules

- Feature API Change More consistent outlier detection API: Add a `score_samples` method in `svm.OneClassSVM`, `ensemble.IsolationForest`, `neighbors.LocalOutlierFactor`, `covariance.EllipticEnvelope`. It allows to access raw score functions from original papers. A new `offset_` parameter allows to link `score_samples` and `decision_function` methods. The `contamination` parameter of `ensemble.IsolationForest` and `neighbors.LocalOutlierFactor` `decision_function` methods is used to define this `offset_` such that outliers (resp. inliers) have negative (resp. positive) `decision_function` values. By default, `contamination` is kept unchanged to 0.1 for a deprecation period. In 0.22, it will be set to “auto”, thus using method-specific score offsets. In `covariance.EllipticEnvelope` `decision_function` method, the `raw_values` parameter is deprecated as the shifted Mahalanobis distance will be always returned in 0.22. #9015 by Nicolas Goix.

- Feature API Change A `behaviour` parameter has been introduced in `ensemble.IsolationForest` to ensure backward compatibility. In the old behaviour, the `decision_function` is independent of the `contamination` parameter. A threshold attribute depending on the `contamination` parameter is thus used. In the new behaviour the `decision_function` is dependent on the `contamination` parameter, in such a way that 0 becomes its natural threshold to detect outliers. Setting behaviour to “old” is deprecated and will not be possible in version 0.22. Beside, the behaviour parameter will be removed in 0.24. #11553 by Nicolas Goix.

- API Change Added convergence warning to `svm.LinearSVC` and `linear_model.LogisticRegression` when `verbose` is set to 0. #10881 by Alexandre Sevin.

- API Change Changed warning type from `UserWarning` to `exceptions.ConvergenceWarning` for failing convergence in `linear_model.logistic_regression_path`, `linear_model.RANSACRegressor`, `linear_model.ridge_regression`, `gaussian_process.GaussianProcessRegressor`, `gaussian_process.GaussianProcessClassifier`, `decomposition.fastica`, `cross_decomposition.PLSCanonical`, `cluster.AffinityPropagation`, and `cluster.Birch`. #10306 by Jonathan Siebert.

### Miscellaneous

- Major Feature A new configuration parameter, `working_memory` was added to control memory consumption limits in chunked operations, such as the new `metrics.pairwise_distances_chunked`. See Limiting Working Memory. #10280 by Joel Nothman and Aman Dalmia.

- Feature The version of `joblib` bundled with Scikit-learn is now 0.12. This uses a new default multiprocessing implementation, named loky. While this may incur some memory and communication overhead, it should provide greater cross-platform stability than relying on Python standard library multiprocessing. #11741 by the Joblib developers, especially Thomas Moreau and Olivier Grisel.

- Feature An environment variable to use the site joblib instead of the vendored one was added (Environment variables). The main API of joblib is now exposed in `sklearn.utils`. #11166 by Gael Varoquaux.

- Feature Add almost complete PyPy 3 support. Known unsupported functionalities are `datasets.load_svmlight_file`, `feature_extraction.FeatureHasher` and `feature_extraction.text.HashingVectorizer`. For running on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required. #11010 by Ronan Lamy and Roman Yurchak.

- Feature A utility method `sklearn.show_versions` was added to print out information relevant for debugging. It includes the user system, the Python executable, the version of the main libraries and BLAS binding information. #11596 by Alexandre Boucaud

- Fix Fixed a bug when setting parameters on meta-estimator, involving both a wrapped estimator and its parameter. #9999 by Marcus Voss and Joel Nothman.

- Fix Fixed a bug where calling `sklearn.base.clone` was not thread safe and could result in a “pop from empty list” error. #9569 by Andreas Müller.

- API Change The default value of `n_jobs` is changed from `1` to `None` in all related functions and classes. `n_jobs=None` means `unset`. It will generally be interpreted as `n_jobs=1`, unless the current `joblib.Parallel` backend context specifies otherwise (See Glossary for additional information). Note that this change happens immediately (i.e., without a deprecation cycle). #11741 by Olivier Grisel.

- Fix Fixed a bug in validation helpers where passing a Dask DataFrame results in an error. #12462 by Zachariah Miller

## Changes to estimator checks

These changes mostly affect library developers.

- Checks for transformers now apply if the estimator implements transform, regardless of whether it inherits from `sklearn.base.TransformerMixin`. #10474 by Joel Nothman.

- Classifiers are now checked for consistency between decision_function and categorical predictions. #10500 by Narine Kokhlikyan.

- Allow tests in `utils.estimator_checks.check_estimator` to test functions that accept pairwise data. #9701 by Kyle Johnson

- Allow `utils.estimator_checks.check_estimator` to check that there is no private settings apart from parameters during estimator initialization. #9378 by Herilalaina Rakotoarison

- The set of checks in `utils.estimator_checks.check_estimator` now includes a `check_set_params` test which checks that `set_params` is equivalent to passing parameters in `__init__` and warns if it encounters parameter validation. #7738 by Alvin Chiang

- Add invariance tests for clustering metrics. #8102 by Ankita Sinha and Guillaume Lemaitre.

- Add `check_methods_subset_invariance` to `check_estimator`, which checks that estimator methods are invariant if applied to a data subset. #10428 by Jonathan Ohayon

- Add tests in `utils.estimator_checks.check_estimator` to check that an estimator can handle read-only memmap input data. #10663 by Arthur Mensch and Loïc Estève.

- `check_sample_weights_pandas_series` now uses 8 rather than 6 samples to accommodate for the default number of clusters in `cluster.KMeans`. #10933 by Johannes Hansen.

- Estimators are now checked for whether `sample_weight=None` equates to `sample_weight=np.ones(...)`. #11558 by Sergul Aydore.

## Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.19, including:

211217613, Aarshay Jain, absolutelyNoWarranty, Adam Greenhall, Adam Kleczewski, Adam Richie-Halford, adelr, AdityaDaflapurkar, Adrin Jalali, Aidan Fitzgerald, aishgrt1, Akash Shivram, Alan Liddell, Alan Yee, Albert Thomas, Alexander Lenail, Alexander-N, Alexandre Boucaud, Alexandre Gramfort, Alexandre Sevin, Alex Egg, Alvaro Perez-Diaz, Amanda, Aman Dalmia, Andreas Bjerre-Nielsen, Andreas Mueller, Andrew Peng, Angus Williams, Aniruddha Dave, annaayzenshtat, Anthony Gitter, Antonio Quinonez, Anubhav Marwaha, Arik Pamnani, Arthur Ozga, Artiem K, Arunava, Arya McCarthy, Attractadore, Aurélien Bellet, Aurélien Geron, Ayush Gupta, Balakumaran Manoharan, Bangda Sun, Barry Hart, Bastian Venthur, Ben Lawson, Benn Roth, Breno Freitas, Brent Yi, brett koonce, Caio Oliveira, Camil Staps, cclauss, Chady Kamar, Charlie Brummitt, Charlie Newey, chris, Chris, Chris Catalfo, Chris Foster, Chris Holdgraf, Christian Braune, Christian Hirsch, Christian Hogan, Christopher Jenness, Clement Joudet, cnx, cwitte, Dallas Card, Dan Barkhorn, Daniel, Daniel Ferreira, Daniel Gomez, Daniel Klevebring, Danielle Shwed, Daniel Mohns, Danil Baibak, Darius Morawiec, David Beach, David Burns, David Kirkby, David Nicholson, David Pickup, Derek, Didi Bar-Zev, diegodlh, Dillon Gardner, Dillon Niederhut, dilutedsauce, dlovell, Dmitry Mottl, Dmitry Petrov, Dor Cohen, Douglas Duhaime, Ekaterina Tuzova, Eric Chang, Eric Dean Sanchez, Erich Schubert, Eunji, Fang-Chieh Chou, FarahSaeed, felix, Félix Raimundo, fenx, filipj8, FrankHui, Franz Wompner, Freija Descamps, frsi, Gabriele Calvo, Gael Varoquaux, Gaurav Dhingra, Georgi Peev, Gil Forsyth, Giovanni Giuseppe Costa, gkevinyen5418, goncalo-rodrigues, Gryllos Prokopis, Guillaume Lemaitre, Guillaume “Vermeille” Sanchez, Gustavo De Mari Pereira, hakaa1, Hanmin Qin, Henry Lin, Hong, Honghe, Hossein Pourbozorg, Hristo, Hunan Rostomyan, iampat, Ivan PANICO, Jaewon Chung, Jake VanderPlas, jakirkham, James Bourbeau, James Malcolm, Jamie Cox, Jan Koch, Jan Margeta, Jan Schlüter, janvanrijn, Jason Wolosonovich, JC Liu, Jeb Bearer, jeremiedbb, Jimmy Wan, Jinkun Wang, Jiongyan Zhang, jjabl, jkleint, Joan Massich, Joël Billaud, Joel Nothman, Johannes Hansen, JohnStott, Jonatan Samoocha, Jonathan Ohayon, Jörg Döpfert, Joris Van den Bossche, Jose Perez-Parras Toledano, josephsalmon, jotasi, jschendel, Julian Kuhlmann, Julien Chaumond, julietcl, Justin Shenk, Karl F, Kasper Primdal Lauritzen, Katrin Leinweber, Kirill, ksemb, Kuai Yu, Kumar Ashutosh, Kyeongpil Kang, Kye Taylor, kyledrogo, Leland McInnes, Léo DS, Liam Geron, Liutong Zhou, Lizao Li, lkjcalc, Loic Esteve, louib, Luciano Viola, Lucija Gregov, Luis Osa, Luis Pedro Coelho, Luke M Craig, Luke Persola, Mabel, Mabel Villalba, Maniteja Nandana, MarkIwanchyshyn, Mark Roth, Markus Müller, MarsGuy, Martin Gubri, martin-hahn, martin-kokos, mathurinm, Matthias Feurer, Max Copeland, Mayur Kulkarni, Meghann Agarwal, Melanie Goetz, Michael A. Alcorn, Minghui Liu, Ming Li, Minh Le, Mohamed Ali Jamaoui, Mohamed Maskani, Mohammad Shahebaz, Muayyad Alsadi, Nabarun Pal, Nagarjuna Kumar, Naoya Kanai, Narendran Santhanam, NarineK, Nathaniel Saul, Nathan Suh, Nicholas Nadeau, P.Eng., AVS, Nick Hoh, Nicolas Goix, Nicolas Hug, Nicolau Werneck, nielsenmarkus11, Nihar Sheth, Nikita Titov, Nilesh Kevlani, Nirvan Anjirbag, notmatthancock, nzw, Oleksandr Pavlyk, oliblum90, Oliver Rausch, Olivier Grisel, Oren Milman, Osaid Rehman Nasir, pasbi, Patrick Fernandes, Patrick Olden, Paul Paczuski, Pedro Morales, Peter, Peter St. John, pierreablin, pietruh, Pinaki Nath Chowdhury, Piotr Szymański, Pradeep Reddy Raamana, Pravar D Mahajan, pravarmahajan, QingYing Chen, Raghav RV, Rajendra arora, RAKOTOARISON Herilalaina, Rameshwar Bhaskaran, RankyLau, Rasul Kerimov, Reiichiro Nakano, Rob, Roman Kosobrodov, Roman Yurchak, Ronan Lamy, rragundez, Rüdiger Busche, Ryan, Sachin Kelkar, Sagnik Bhattacharya, Sailesh Choyal, Sam Radhakrishnan, Sam Steingold, Samuel Bell, Samuel O. Ronsin, Saqib Nizam Shamsi, SATISH J, Saurabh Gupta, Scott Gigante, Sebastian Flennerhag, Sebastian Raschka, Sebastien Dubois, Sébastien Lerique, Sebastin Santy, Sergey Feldman, Sergey Melderis, Sergul Aydore, Shahebaz, Shalil Awaley, Shangwu Yao, Sharad Vijalapuram, Sharan Yalburgi, shenhanc78, Shivam Rastogi, Shu Haoran, siftikha, Sinclert Pérez, SolutusImmensus, Somya Anand, srajan paliwal, Sriharsha Hatwar, Sri Krishna, Stefan van der Walt, Stephen McDowell, Steven Brown, syonekura, Taehoon Lee, Takanori Hayashi, tarcusx, Taylor G Smith, theriley106, Thomas, Thomas Fan, Thomas Heavey, Tobias Madsen, tobycheese, Tom Augspurger, Tom Dupré la Tour, Tommy, Trevor Stephens, Trishnendu Ghorai, Tulio Casagrande, twosigmajab, Umar Farouk Umar, Urvang Patel, Utkarsh Upadhyay, Vadim Markovtsev, Varun Agrawal, Vathsala Achar, Vilhelm von Ehrenheim, Vinayak Mehta, Vinit, Vinod Kumar L, Viraj Mavani, Viraj Navkal, Vivek Kumar, Vlad Niculae, vqean3, Vrishank Bhardwaj, vufg, wallygauze, Warut Vijitbenjaronk, wdevazelhes, Wenhao Zhang, Wes Barnett, Will, William de Vazelhes, Will Rosenfeld, Xin Xiong, Yiming (Paul) Li, ymazari, Yufeng, Zach Griffith, Zé Vinícius, Zhenqing Hu, Zhiqing Xiao, Zijie (ZJ) Poh