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# Version 1.1.3

October 2022

This bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2. Notable changes include:

- Fix Include `msvcp140.dll` in the scikit-learn wheels since it has been removed in the latest SciPy wheels. #24631 by Chiara Marmo.

- Enhancement Create wheels for Python 3.11. #24446 by Chiara Marmo.

Other bug fixes will be available in the next 1.2 release, which will be released in the coming weeks.

Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead.

# Version 1.1.2

August 2022

## 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.

- Fix `manifold.TSNE` now throws a `ValueError` when fit with `perplexity>=n_samples` to ensure mathematical correctness of the algorithm. #10805 by Mathias Andersen and #23471 by Meekail Zain.

## Changelog

- Fix A default HTML representation is shown for meta-estimators with invalid parameters. #24015 by Thomas Fan.

- Fix Add support for F-contiguous arrays for estimators and functions whose back-end have been changed in 1.1. #23990 by Julien Jerphanion.

- Fix Wheels are now available for MacOS 10.9 and greater. #23833 by Thomas Fan.

### `sklearn.base`

- Fix The `get_params` method of the `base.BaseEstimator` class now supports estimators with `type`-type params that have the `get_params` method. #24017 by Henry Sorsky.

### `sklearn.cluster`

- Fix Fixed a bug in `cluster.Birch` that could trigger an error when splitting a node if there are duplicates in the dataset. #23395 by Jérémie du Boisberranger.

### `sklearn.feature_selection`

- Fix `feature_selection.SelectFromModel` defaults to selection threshold 1e-5 when the estimator is either `linear_model.ElasticNet` or `linear_model.ElasticNetCV` with `l1_ratio` equals 1 or `linear_model.LassoCV`. #23636 by Hao Chun Chang.

### `sklearn.impute`

- Fix `impute.SimpleImputer` uses the dtype seen in `fit` for `transform` when the dtype is object. #22063 by Thomas Fan.

### `sklearn.linear_model`

- Fix Use dtype-aware tolerances for the validation of gram matrices (passed by users or precomputed). #22059 by Malte S. Kurz.

- Fix Fixed an error in `linear_model.LogisticRegression` with `solver="newton-cg"`, `fit_intercept=True`, and a single feature. #23608 by Tom Dupre la Tour.

### `sklearn.manifold`

- Fix `manifold.TSNE` now throws a `ValueError` when fit with `perplexity>=n_samples` to ensure mathematical correctness of the algorithm. #10805 by Mathias Andersen and #23471 by Meekail Zain.

### `sklearn.metrics`

- Fix Fixed error message of `metrics.coverage_error` for 1D array input. #23548 by Hao Chun Chang.

### `sklearn.preprocessing`

- Fix `preprocessing.OrdinalEncoder.inverse_transform` correctly handles use cases where `unknown_value` or `encoded_missing_value` is `nan`. #24087 by Thomas Fan.

### `sklearn.tree`

- Fix Fixed invalid memory access bug during fit in `tree.DecisionTreeRegressor` and `tree.DecisionTreeClassifier`. #23273 by Thomas Fan.

# Version 1.1.1

May 2022

## Changelog

- Enhancement The error message is improved when importing `model_selection.HalvingGridSearchCV`, `model_selection.HalvingRandomSearchCV`, or `impute.IterativeImputer` without importing the experimental flag. #23194 by Thomas Fan.

- Enhancement Added an extension in doc/conf.py to automatically generate the list of estimators that handle NaN values. #23198 by Lise Kleiber, Zhehao Liu and Chiara Marmo.

### `sklearn.datasets`

- Fix Avoid timeouts in `datasets.fetch_openml` by not passing a `timeout` argument, #23358 by Loïc Estève.

### `sklearn.decomposition`

- Fix Avoid spurious warning in `decomposition.IncrementalPCA` when `n_samples == n_components`. #23264 by Lucy Liu.

### `sklearn.feature_selection`

- Fix The `partial_fit` method of `feature_selection.SelectFromModel` now conducts validation for `max_features` and `feature_names_in` parameters. #23299 by Long Bao.

### `sklearn.metrics`

- Fix Fixes `metrics.precision_recall_curve` to compute precision-recall at 100% recall. The Precision-Recall curve now displays the last point corresponding to a classifier that always predicts the positive class: recall=100% and precision=class balance. #23214 by Stéphane Collot and Max Baak.

### `sklearn.preprocessing`

- Fix `preprocessing.PolynomialFeatures` with `degree` equal to 0 will raise error when `include_bias` is set to False, and outputs a single constant array when `include_bias` is set to True. #23370 by Zhehao Liu.

### `sklearn.tree`

- Fix Fixes performance regression with low cardinality features for `tree.DecisionTreeClassifier`, `tree.DecisionTreeRegressor`, `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor`, `ensemble.GradientBoostingClassifier`, and `ensemble.GradientBoostingRegressor`. #23410 by Loïc Estève.

### `sklearn.utils`

- Fix `utils.class_weight.compute_sample_weight` now works with sparse `y`. #23115 by kernc.

# Version 1.1.0

May 2022

For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.1.

## Legend for changelogs

- Major Feature : something big that you couldn’t do before.

- Feature : something that you couldn’t do before.

- Efficiency : an existing feature now may not require as much computation or memory.

- Enhancement : a miscellaneous minor improvement.

- Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.

- API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

## Minimal dependencies

Version 1.1.0 of scikit-learn requires python 3.8+, numpy 1.17.3+ and scipy 1.3.2+. Optional minimal dependency is matplotlib 3.1.2+.

## 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.

- Efficiency `cluster.KMeans` now defaults to `algorithm="lloyd"` instead of `algorithm="auto"`, which was equivalent to `algorithm="elkan"`. Lloyd’s algorithm and Elkan’s algorithm converge to the same solution, up to numerical rounding errors, but in general Lloyd’s algorithm uses much less memory, and it is often faster.

- Efficiency Fitting `tree.DecisionTreeClassifier`, `tree.DecisionTreeRegressor`, `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor`, `ensemble.GradientBoostingClassifier`, and `ensemble.GradientBoostingRegressor` is on average 15% faster than in previous versions thanks to a new sort algorithm to find the best split. Models might be different because of a different handling of splits with tied criterion values: both the old and the new sorting algorithm are unstable sorting algorithms. #22868 by Thomas Fan.

- Fix The eigenvectors initialization for `cluster.SpectralClustering` and `manifold.SpectralEmbedding` now samples from a Gaussian when using the `'amg'` or `'lobpcg'` solver. This change improves numerical stability of the solver, but may result in a different model.

- Fix `feature_selection.f_regression` and `feature_selection.r_regression` will now returned finite score by default instead of `np.nan` and `np.inf` for some corner case. You can use `force_finite=False` if you really want to get non-finite values and keep the old behavior.

- Fix Panda’s DataFrames with all non-string columns such as a MultiIndex no longer warns when passed into an Estimator. Estimators will continue to ignore the column names in DataFrames with non-string columns. For `feature_names_in_` to be defined, columns must be all strings. #22410 by Thomas Fan.

- Fix `preprocessing.KBinsDiscretizer` changed handling of bin edges slightly, which might result in a different encoding with the same data.

- Fix `calibration.calibration_curve` changed handling of bin edges slightly, which might result in a different output curve given the same data.

- Fix `discriminant_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior.

- Fix `feature_selection.SelectFromModel.fit` and `feature_selection.SelectFromModel.partial_fit` can now be called with `prefit=True`. `estimators_` will be a deep copy of `estimator` when `prefit=True`. #23271 by Guillaume Lemaitre.

## Changelog

- Efficiency Low-level routines for reductions on pairwise distances for dense float64 datasets have been refactored. The following functions and estimators now benefit from improved performances in terms of hardware scalability and speed-ups:

  - `sklearn.metrics.pairwise_distances_argmin`

  - `sklearn.metrics.pairwise_distances_argmin_min`

  - `sklearn.cluster.AffinityPropagation`

  - `sklearn.cluster.Birch`

  - `sklearn.cluster.MeanShift`

  - `sklearn.cluster.OPTICS`

  - `sklearn.cluster.SpectralClustering`

  - `sklearn.feature_selection.mutual_info_regression`

  - `sklearn.neighbors.KNeighborsClassifier`

  - `sklearn.neighbors.KNeighborsRegressor`

  - `sklearn.neighbors.RadiusNeighborsClassifier`

  - `sklearn.neighbors.RadiusNeighborsRegressor`

  - `sklearn.neighbors.LocalOutlierFactor`

  - `sklearn.neighbors.NearestNeighbors`

  - `sklearn.manifold.Isomap`

  - `sklearn.manifold.LocallyLinearEmbedding`

  - `sklearn.manifold.TSNE`

  - `sklearn.manifold.trustworthiness`

  - `sklearn.semi_supervised.LabelPropagation`

  - `sklearn.semi_supervised.LabelSpreading`

  For instance `sklearn.neighbors.NearestNeighbors.kneighbors` and `sklearn.neighbors.NearestNeighbors.radius_neighbors` can respectively be up to ×20 and ×5 faster than previously on a laptop.

  Moreover, implementations of those two algorithms are now suitable for machine with many cores, making them usable for datasets consisting of millions of samples.

  #21987, #22064, #22065, #22288 and #22320 by Julien Jerphanion.

- Enhancement All scikit-learn models now generate a more informative error message when some input contains unexpected `NaN` or infinite values. In particular the message contains the input name (“X”, “y” or “sample_weight”) and if an unexpected `NaN` value is found in `X`, the error message suggests potential solutions. #21219 by Olivier Grisel.

- Enhancement All scikit-learn models now generate a more informative error message when setting invalid hyper-parameters with `set_params`. #21542 by Olivier Grisel.

- Enhancement Removes random unique identifiers in the HTML representation. With this change, jupyter notebooks are reproducible as long as the cells are run in the same order. #23098 by Thomas Fan.

- Fix Estimators with `non_deterministic` tag set to `True` will skip both `check_methods_sample_order_invariance` and `check_methods_subset_invariance` tests. #22318 by Zhehao Liu.

- API Change The option for using the log loss, aka binomial or multinomial deviance, via the `loss` parameters was made more consistent. The preferred way is by setting the value to `"log_loss"`. Old option names are still valid and produce the same models, but are deprecated and will be removed in version 1.3.

  - For `ensemble.GradientBoostingClassifier`, the `loss` parameter name “deviance” is deprecated in favor of the new name “log_loss”, which is now the default. #23036 by Christian Lorentzen.

  - For `ensemble.HistGradientBoostingClassifier`, the `loss` parameter names “auto”, “binary_crossentropy” and “categorical_crossentropy” are deprecated in favor of the new name “log_loss”, which is now the default. #23040 by Christian Lorentzen.

  - For `linear_model.SGDClassifier`, the `loss` parameter name “log” is deprecated in favor of the new name “log_loss”. #23046 by Christian Lorentzen.

- API Change Rich html representation of estimators is now enabled by default in Jupyter notebooks. It can be deactivated by setting `display='text'` in `sklearn.set_config`. #22856 by Jérémie du Boisberranger.

### `sklearn.calibration`

- Enhancement `calibration.calibration_curve` accepts a parameter `pos_label` to specify the positive class label. #21032 by Guillaume Lemaitre.

- Enhancement `calibration.CalibratedClassifierCV.fit` now supports passing `fit_params`, which are routed to the `base_estimator`. #18170 by Benjamin Bossan.

- Enhancement `calibration.CalibrationDisplay` accepts a parameter `pos_label` to add this information to the plot. #21038 by Guillaume Lemaitre.

- Fix `calibration.calibration_curve` handles bin edges more consistently now. #14975 by Andreas Müller and #22526 by Meekail Zain.

- API Change `calibration.calibration_curve`’s `normalize` parameter is now deprecated and will be removed in version 1.3. It is recommended that a proper probability (i.e. a classifier’s predict_proba positive class) is used for `y_prob`. #23095 by Jordan Silke.

### `sklearn.cluster`

- Major Feature `cluster.BisectingKMeans` introducing Bisecting K-Means algorithm #20031 by Michal Krawczyk, Tom Dupre la Tour and Jérémie du Boisberranger.

- Enhancement `cluster.SpectralClustering` and `cluster.spectral_clustering` now include the new `'cluster_qr'` method that clusters samples in the embedding space as an alternative to the existing `'kmeans'` and `'discrete'` methods. See `cluster.spectral_clustering` for more details. #21148 by Andrew Knyazev.

- Enhancement Adds get_feature_names_out to `cluster.Birch`, `cluster.FeatureAgglomeration`, `cluster.KMeans`, `cluster.MiniBatchKMeans`. #22255 by Thomas Fan.

- Enhancement `cluster.SpectralClustering` now raises consistent error messages when passed invalid values for `n_clusters`, `n_init`, `gamma`, `n_neighbors`, `eigen_tol` or `degree`. #21881 by Hugo Vassard.

- Enhancement `cluster.AffinityPropagation` now returns cluster centers and labels if they exist, even if the model has not fully converged. When returning these potentially-degenerate cluster centers and labels, a new warning message is shown. If no cluster centers were constructed, then the cluster centers remain an empty list with labels set to `-1` and the original warning message is shown. #22217 by Meekail Zain.

- Efficiency In `cluster.KMeans`, the default `algorithm` is now `"lloyd"` which is the full classical EM-style algorithm. Both `"auto"` and `"full"` are deprecated and will be removed in version 1.3. They are now aliases for `"lloyd"`. The previous default was `"auto"`, which relied on Elkan’s algorithm. Lloyd’s algorithm uses less memory than Elkan’s, it is faster on many datasets, and its results are identical, hence the change. #21735 by Aurélien Geron.

- Fix `cluster.KMeans`’s `init` parameter now properly supports array-like input and NumPy string scalars. #22154 by Thomas Fan.

### `sklearn.compose`

- Fix `compose.ColumnTransformer` now removes validation errors from `__init__` and `set_params` methods. #22537 by iofall and Arisa Y..

- Fix get_feature_names_out functionality in `compose.ColumnTransformer` was broken when columns were specified using `slice`. This is fixed in #22775 and #22913 by randomgeek78.

### `sklearn.covariance`

- Fix `covariance.GraphicalLassoCV` now accepts NumPy array for the parameter `alphas`. #22493 by Guillaume Lemaitre.

### `sklearn.cross_decomposition`

- Enhancement the `inverse_transform` method of `cross_decomposition.PLSRegression`, `cross_decomposition.PLSCanonical` and `cross_decomposition.CCA` now allows reconstruction of a `X` target when a `Y` parameter is given. #19680 by Robin Thibaut.

- Enhancement Adds get_feature_names_out to all transformers in the `cross_decomposition` module: `cross_decomposition.CCA`, `cross_decomposition.PLSSVD`, `cross_decomposition.PLSRegression`, and `cross_decomposition.PLSCanonical`. #22119 by Thomas Fan.

- Fix The shape of the coef_ attribute of `cross_decomposition.CCA`, `cross_decomposition.PLSCanonical` and `cross_decomposition.PLSRegression` will change in version 1.3, from `(n_features, n_targets)` to `(n_targets, n_features)`, to be consistent with other linear models and to make it work with interface expecting a specific shape for `coef_` (e.g. `feature_selection.RFE`). #22016 by Guillaume Lemaitre.

- API Change add the fitted attribute `intercept_` to `cross_decomposition.PLSCanonical`, `cross_decomposition.PLSRegression`, and `cross_decomposition.CCA`. The method `predict` is indeed equivalent to `Y = X @ coef_ + intercept_`. #22015 by Guillaume Lemaitre.

### `sklearn.datasets`

- Feature `datasets.load_files` now accepts a ignore list and an allow list based on file extensions. #19747 by Tony Attalla and #22498 by Meekail Zain.

- Enhancement `datasets.make_swiss_roll` now supports the optional argument hole; when set to True, it returns the swiss-hole dataset. #21482 by Sebastian Pujalte.

- Enhancement `datasets.make_blobs` no longer copies data during the generation process, therefore uses less memory. #22412 by Zhehao Liu.

- Enhancement `datasets.load_diabetes` now accepts the parameter `scaled`, to allow loading unscaled data. The scaled version of this dataset is now computed from the unscaled data, and can produce slightly different results that in previous version (within a 1e-4 absolute tolerance). #16605 by Mandy Gu.

- Enhancement `datasets.fetch_openml` now has two optional arguments `n_retries` and `delay`. By default, `datasets.fetch_openml` will retry 3 times in case of a network failure with a delay between each try. #21901 by Rileran.

- Fix `datasets.fetch_covtype` is now concurrent-safe: data is downloaded to a temporary directory before being moved to the data directory. #23113 by Ilion Beyst.

- API Change `datasets.make_sparse_coded_signal` now accepts a parameter `data_transposed` to explicitly specify the shape of matrix `X`. The default behavior `True` is to return a transposed matrix `X` corresponding to a `(n_features, n_samples)` shape. The default value will change to `False` in version 1.3. #21425 by Gabriel Stefanini Vicente.

### `sklearn.decomposition`

- Major Feature Added a new estimator `decomposition.MiniBatchNMF`. It is a faster but less accurate version of non-negative matrix factorization, better suited for large datasets. #16948 by Chiara Marmo, Patricio Cerda and Jérémie du Boisberranger.

- Enhancement `decomposition.dict_learning`, `decomposition.dict_learning_online` and `decomposition.sparse_encode` preserve dtype for `numpy.float32`. `decomposition.DictionaryLearning`, `decomposition.MiniBatchDictionaryLearning` and `decomposition.SparseCoder` preserve dtype for `numpy.float32`. #22002 by Takeshi Oura.

- Enhancement `decomposition.PCA` exposes a parameter `n_oversamples` to tune `utils.extmath.randomized_svd` and get accurate results when the number of features is large. #21109 by Smile.

- Enhancement The `decomposition.MiniBatchDictionaryLearning` and `decomposition.dict_learning_online` have been refactored and now have a stopping criterion based on a small change of the dictionary or objective function, controlled by the new `max_iter`, `tol` and `max_no_improvement` parameters. In addition, some of their parameters and attributes are deprecated.

  - the `n_iter` parameter of both is deprecated. Use `max_iter` instead.

  - the `iter_offset`, `return_inner_stats`, `inner_stats` and `return_n_iter` parameters of `decomposition.dict_learning_online` serve internal purpose and are deprecated.

  - the `inner_stats_`, `iter_offset_` and `random_state_` attributes of `decomposition.MiniBatchDictionaryLearning` serve internal purpose and are deprecated.

  - the default value of the `batch_size` parameter of both will change from 3 to 256 in version 1.3.

  #18975 by Jérémie du Boisberranger.

- Enhancement `decomposition.SparsePCA` and `decomposition.MiniBatchSparsePCA` preserve dtype for `numpy.float32`. #22111 by Takeshi Oura.

- Enhancement `decomposition.TruncatedSVD` now allows `n_components == n_features`, if `algorithm='randomized'`. #22181 by Zach Deane-Mayer.

- Enhancement Adds get_feature_names_out to all transformers in the `decomposition` module: `decomposition.DictionaryLearning`, `decomposition.FactorAnalysis`, `decomposition.FastICA`, `decomposition.IncrementalPCA`, `decomposition.KernelPCA`, `decomposition.LatentDirichletAllocation`, `decomposition.MiniBatchDictionaryLearning`, `decomposition.MiniBatchSparsePCA`, `decomposition.NMF`, `decomposition.PCA`, `decomposition.SparsePCA`, and `decomposition.TruncatedSVD`. #21334 by Thomas Fan.

- Enhancement `decomposition.TruncatedSVD` exposes the parameter `n_oversamples` and `power_iteration_normalizer` to tune `utils.extmath.randomized_svd` and get accurate results when the number of features is large, the rank of the matrix is high, or other features of the matrix make low rank approximation difficult. #21705 by Jay S. Stanley III.

- Enhancement `decomposition.PCA` exposes the parameter `power_iteration_normalizer` to tune `utils.extmath.randomized_svd` and get more accurate results when low rank approximation is difficult. #21705 by Jay S. Stanley III.

- Fix `decomposition.FastICA` now validates input parameters in `fit` instead of `__init__`. #21432 by Hannah Bohle and Maren Westermann.

- Fix `decomposition.FastICA` now accepts `np.float32` data without silent upcasting. The dtype is preserved by `fit` and `fit_transform` and the main fitted attributes use a dtype of the same precision as the training data. #22806 by Jihane Bennis and Olivier Grisel.

- Fix `decomposition.FactorAnalysis` now validates input parameters in `fit` instead of `__init__`. #21713 by Haya and Krum Arnaudov.

- Fix `decomposition.KernelPCA` now validates input parameters in `fit` instead of `__init__`. #21567 by Maggie Chege.

- Fix `decomposition.PCA` and `decomposition.IncrementalPCA` more safely calculate precision using the inverse of the covariance matrix if `self.noise_variance_` is zero. #22300 by Meekail Zain and #15948 by @sysuresh.

- Fix Greatly reduced peak memory usage in `decomposition.PCA` when calling `fit` or `fit_transform`. #22553 by Meekail Zain.

- API Change `decomposition.FastICA` now supports unit variance for whitening. The default value of its `whiten` argument will change from `True` (which behaves like `'arbitrary-variance'`) to `'unit-variance'` in version 1.3. #19490 by Facundo Ferrin and Julien Jerphanion.

### `sklearn.discriminant_analysis`

- Enhancement Adds get_feature_names_out to `discriminant_analysis.LinearDiscriminantAnalysis`. #22120 by Thomas Fan.

- Fix `discriminant_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior. #15984 by Okon Samuel and #22696 by Meekail Zain.

### `sklearn.dummy`

- Fix `dummy.DummyRegressor` no longer overrides the `constant` parameter during `fit`. #22486 by Thomas Fan.

### `sklearn.ensemble`

- Major Feature Added additional option `loss="quantile"` to `ensemble.HistGradientBoostingRegressor` for modelling quantiles. The quantile level can be specified with the new parameter `quantile`. #21800 and #20567 by Christian Lorentzen.

- Efficiency `fit` of `ensemble.GradientBoostingClassifier` and `ensemble.GradientBoostingRegressor` now calls `utils.check_array` with parameter `force_all_finite=False` for non initial warm-start runs as it has already been checked before. #22159 by Geoffrey Paris.

- Enhancement `ensemble.HistGradientBoostingClassifier` is faster, for binary and in particular for multiclass problems thanks to the new private loss function module. #20811, #20567 and #21814 by Christian Lorentzen.

- Enhancement Adds support to use pre-fit models with `cv="prefit"` in `ensemble.StackingClassifier` and `ensemble.StackingRegressor`. #16748 by Siqi He and #22215 by Meekail Zain.

- Enhancement `ensemble.RandomForestClassifier` and `ensemble.ExtraTreesClassifier` have the new `criterion="log_loss"`, which is equivalent to `criterion="entropy"`. #23047 by Christian Lorentzen.

- Enhancement Adds get_feature_names_out to `ensemble.VotingClassifier`, `ensemble.VotingRegressor`, `ensemble.StackingClassifier`, and `ensemble.StackingRegressor`. #22695 and #22697 by Thomas Fan.

- Enhancement `ensemble.RandomTreesEmbedding` now has an informative get_feature_names_out function that includes both tree index and leaf index in the output feature names. #21762 by Zhehao Liu and Thomas Fan.

- Efficiency Fitting a `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor`, `ensemble.ExtraTreesClassifier`, `ensemble.ExtraTreesRegressor`, and `ensemble.RandomTreesEmbedding` is now faster in a multiprocessing setting, especially for subsequent fits with `warm_start` enabled. #22106 by Pieter Gijsbers.

- Fix Change the parameter `validation_fraction` in `ensemble.GradientBoostingClassifier` and `ensemble.GradientBoostingRegressor` so that an error is raised if anything other than a float is passed in as an argument. #21632 by Genesis Valencia.

- Fix Removed a potential source of CPU oversubscription in `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` when CPU resource usage is limited, for instance using cgroups quota in a docker container. #22566 by Jérémie du Boisberranger.

- Fix `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` no longer warns when fitting on a pandas DataFrame with a non-default `scoring` parameter and early_stopping enabled. #22908 by Thomas Fan.

- Fix Fixes HTML repr for `ensemble.StackingClassifier` and `ensemble.StackingRegressor`. #23097 by Thomas Fan.

- API Change The attribute `loss_` of `ensemble.GradientBoostingClassifier` and `ensemble.GradientBoostingRegressor` has been deprecated and will be removed in version 1.3. #23079 by Christian Lorentzen.

- API Change Changed the default of `max_features` to 1.0 for `ensemble.RandomForestRegressor` and to `"sqrt"` for `ensemble.RandomForestClassifier`. Note that these give the same fit results as before, but are much easier to understand. The old default value `"auto"` has been deprecated and will be removed in version 1.3. The same changes are also applied for `ensemble.ExtraTreesRegressor` and `ensemble.ExtraTreesClassifier`. #20803 by Brian Sun.

- Efficiency Improve runtime performance of `ensemble.IsolationForest` by skipping repetitive input checks. #23149 by Zhehao Liu.

### `sklearn.feature_extraction`

- Feature `feature_extraction.FeatureHasher` now supports PyPy. #23023 by Thomas Fan.

- Fix `feature_extraction.FeatureHasher` now validates input parameters in `transform` instead of `__init__`. #21573 by Hannah Bohle and Maren Westermann.

- Fix `feature_extraction.text.TfidfVectorizer` now does not create a `feature_extraction.text.TfidfTransformer` at `__init__` as required by our API. #21832 by Guillaume Lemaitre.

### `sklearn.feature_selection`

- Feature Added auto mode to `feature_selection.SequentialFeatureSelector`. If the argument `n_features_to_select` is `'auto'`, select features until the score improvement does not exceed the argument `tol`. The default value of `n_features_to_select` changed from `None` to `'warn'` in 1.1 and will become `'auto'` in 1.3. `None` and `'warn'` will be removed in 1.3. #20145 by murata-yu.

- Feature Added the ability to pass callables to the `max_features` parameter of `feature_selection.SelectFromModel`. Also introduced new attribute `max_features_` which is inferred from `max_features` and the data during `fit`. If `max_features` is an integer, then `max_features_ = max_features`. If `max_features` is a callable, then `max_features_ = max_features(X)`. #22356 by Meekail Zain.

- Enhancement `feature_selection.GenericUnivariateSelect` preserves float32 dtype. #18482 by Thierry Gameiro and Daniel Kharsa and #22370 by Meekail Zain.

- Enhancement Add a parameter `force_finite` to `feature_selection.f_regression` and `feature_selection.r_regression`. This parameter allows to force the output to be finite in the case where a feature or a the target is constant or that the feature and target are perfectly correlated (only for the F-statistic). #17819 by Juan Carlos Alfaro Jiménez.

- Efficiency Improve runtime performance of `feature_selection.chi2` with boolean arrays. #22235 by Thomas Fan.

- Efficiency Reduced memory usage of `feature_selection.chi2`. #21837 by Louis Wagner.

### `sklearn.gaussian_process`

- Fix `predict` and `sample_y` methods of `gaussian_process.GaussianProcessRegressor` now return arrays of the correct shape in single-target and multi-target cases, and for both `normalize_y=False` and `normalize_y=True`. #22199 by Guillaume Lemaitre, Aidar Shakerimoff and Tenavi Nakamura-Zimmerer.

- Fix `gaussian_process.GaussianProcessClassifier` raises a more informative error if `CompoundKernel` is passed via `kernel`. #22223 by MarcoM.

### `sklearn.impute`

- Enhancement `impute.SimpleImputer` now warns with feature names when features which are skipped due to the lack of any observed values in the training set. #21617 by Christian Ritter.

- Enhancement Added support for `pd.NA` in `impute.SimpleImputer`. #21114 by Ying Xiong.

- Enhancement Adds get_feature_names_out to `impute.SimpleImputer`, `impute.KNNImputer`, `impute.IterativeImputer`, and `impute.MissingIndicator`. #21078 by Thomas Fan.

- API Change The `verbose` parameter was deprecated for `impute.SimpleImputer`. A warning will always be raised upon the removal of empty columns. #21448 by Oleh Kozynets and Christian Ritter.

### `sklearn.inspection`

- Feature Add a display to plot the boundary decision of a classifier by using the method `inspection.DecisionBoundaryDisplay.from_estimator`. #16061 by Thomas Fan.

- Enhancement In `inspection.PartialDependenceDisplay.from_estimator`, allow `kind` to accept a list of strings to specify which type of plot to draw for each feature interaction. #19438 by Guillaume Lemaitre.

- Enhancement `inspection.PartialDependenceDisplay.from_estimator`, `inspection.PartialDependenceDisplay.plot`, and `inspection.plot_partial_dependence` now support plotting centered Individual Conditional Expectation (cICE) and centered PDP curves controlled by setting the parameter `centered`. #18310 by Johannes Elfner and Guillaume Lemaitre.

### `sklearn.isotonic`

- Enhancement Adds get_feature_names_out to `isotonic.IsotonicRegression`. #22249 by Thomas Fan.

### `sklearn.kernel_approximation`

- Enhancement Adds get_feature_names_out to `kernel_approximation.AdditiveChi2Sampler`. `kernel_approximation.Nystroem`, `kernel_approximation.PolynomialCountSketch`, `kernel_approximation.RBFSampler`, and `kernel_approximation.SkewedChi2Sampler`. #22137 and #22694 by Thomas Fan.

### `sklearn.linear_model`

- Feature `linear_model.ElasticNet`, `linear_model.ElasticNetCV`, `linear_model.Lasso` and `linear_model.LassoCV` support `sample_weight` for sparse input `X`. #22808 by Christian Lorentzen.

- Feature `linear_model.Ridge` with `solver="lsqr"` now supports to fit sparse input with `fit_intercept=True`. #22950 by Christian Lorentzen.

- Enhancement `linear_model.QuantileRegressor` support sparse input for the highs based solvers. #21086 by Venkatachalam Natchiappan. In addition, those solvers now use the CSC matrix right from the beginning which speeds up fitting. #22206 by Christian Lorentzen.

- Enhancement `linear_model.LogisticRegression` is faster for `solvers="lbfgs"` and `solver="newton-cg"`, for binary and in particular for multiclass problems thanks to the new private loss function module. In the multiclass case, the memory consumption has also been reduced for these solvers as the target is now label encoded (mapped to integers) instead of label binarized (one-hot encoded). The more classes, the larger the benefit. #21808, #20567 and #21814 by Christian Lorentzen.

- Enhancement `linear_model.GammaRegressor`, `linear_model.PoissonRegressor` and `linear_model.TweedieRegressor` are faster for `solvers="lbfgs"`. #22548, #21808 and #20567 by Christian Lorentzen.

- Enhancement Rename parameter `base_estimator` to `estimator` in `linear_model.RANSACRegressor` to improve readability and consistency. `base_estimator` is deprecated and will be removed in 1.3. #22062 by Adrian Trujillo.

- Enhancement `linear_model.ElasticNet` and and other linear model classes using coordinate descent show error messages when non-finite parameter weights are produced. #22148 by Christian Ritter and Norbert Preining.

- Enhancement `linear_model.ElasticNet` and `linear_model.Lasso` now raise consistent error messages when passed invalid values for `l1_ratio`, `alpha`, `max_iter` and `tol`. #22240 by Arturo Amor.

- Enhancement `linear_model.BayesianRidge` and `linear_model.ARDRegression` now preserve float32 dtype. #9087 by Arthur Imbert and #22525 by Meekail Zain.

- Enhancement `linear_model.RidgeClassifier` is now supporting multilabel classification. #19689 by Guillaume Lemaitre.

- Enhancement `linear_model.RidgeCV` and `linear_model.RidgeClassifierCV` now raise consistent error message when passed invalid values for `alphas`. #21606 by Arturo Amor.

- Enhancement `linear_model.Ridge` and `linear_model.RidgeClassifier` now raise consistent error message when passed invalid values for `alpha`, `max_iter` and `tol`. #21341 by Arturo Amor.

- Enhancement `linear_model.orthogonal_mp_gram` preservse dtype for `numpy.float32`. #22002 by Takeshi Oura.

- Fix `linear_model.LassoLarsIC` now correctly computes AIC and BIC. An error is now raised when `n_features > n_samples` and when the noise variance is not provided. #21481 by Guillaume Lemaitre and Andrés Babino.

- Fix `linear_model.TheilSenRegressor` now validates input parameter `max_subpopulation` in `fit` instead of `__init__`. #21767 by Maren Westermann.

- Fix `linear_model.ElasticNetCV` now produces correct warning when `l1_ratio=0`. #21724 by Yar Khine Phyo.

- Fix `linear_model.LogisticRegression` and `linear_model.LogisticRegressionCV` now set the `n_iter_` attribute with a shape that respects the docstring and that is consistent with the shape obtained when using the other solvers in the one-vs-rest setting. Previously, it would record only the maximum of the number of iterations for each binary sub-problem while now all of them are recorded. #21998 by Olivier Grisel.

- Fix The property `family` of `linear_model.TweedieRegressor` is not validated in `__init__` anymore. Instead, this (private) property is deprecated in `linear_model.GammaRegressor`, `linear_model.PoissonRegressor` and `linear_model.TweedieRegressor`, and will be removed in 1.3. #22548 by Christian Lorentzen.

- Fix The `coef_` and `intercept_` attributes of `linear_model.LinearRegression` are now correctly computed in the presence of sample weights when the input is sparse. #22891 by Jérémie du Boisberranger.

- Fix The `coef_` and `intercept_` attributes of `linear_model.Ridge` with `solver="sparse_cg"` and `solver="lbfgs"` are now correctly computed in the presence of sample weights when the input is sparse. #22899 by Jérémie du Boisberranger.

- Fix `linear_model.SGDRegressor` and `linear_model.SGDClassifier` now computes the validation error correctly when early stopping is enabled. #23256 by Zhehao Liu.

- API Change `linear_model.LassoLarsIC` now exposes `noise_variance` as a parameter in order to provide an estimate of the noise variance. This is particularly relevant when `n_features > n_samples` and the estimator of the noise variance cannot be computed. #21481 by Guillaume Lemaitre.

### `sklearn.manifold`

- Feature `manifold.Isomap` now supports radius-based neighbors via the `radius` argument. #19794 by Zhehao Liu.

- Enhancement `manifold.spectral_embedding` and `manifold.SpectralEmbedding` supports `np.float32` dtype and will preserve this dtype. #21534 by Andrew Knyazev.

- Enhancement Adds get_feature_names_out to `manifold.Isomap` and `manifold.LocallyLinearEmbedding`. #22254 by Thomas Fan.

- Enhancement added `metric_params` to `manifold.TSNE` constructor for additional parameters of distance metric to use in optimization. #21805 by Jeanne Dionisi and #22685 by Meekail Zain.

- Enhancement `manifold.trustworthiness` raises an error if `n_neighbours >= n_samples / 2` to ensure a correct support for the function. #18832 by Hong Shao Yang and #23033 by Meekail Zain.

- Fix `manifold.spectral_embedding` now uses Gaussian instead of the previous uniform on [0, 1] random initial approximations to eigenvectors in eigen_solvers `lobpcg` and `amg` to improve their numerical stability. #21565 by Andrew Knyazev.

### `sklearn.metrics`

- Feature `metrics.r2_score` and `metrics.explained_variance_score` have a new `force_finite` parameter. Setting this parameter to `False` will return the actual non-finite score in case of perfect predictions or constant `y_true`, instead of the finite approximation (`1.0` and `0.0` respectively) currently returned by default. #17266 by Sylvain Marié.

- Feature `metrics.d2_pinball_score` and `metrics.d2_absolute_error_score` calculate the \(D^2\) regression score for the pinball loss and the absolute error respectively. `metrics.d2_absolute_error_score` is a special case of `metrics.d2_pinball_score` with a fixed quantile parameter `alpha=0.5` for ease of use and discovery. The \(D^2\) scores are generalizations of the `r2_score` and can be interpreted as the fraction of deviance explained. #22118 by Ohad Michel.

- Enhancement `metrics.top_k_accuracy_score` raises an improved error message when `y_true` is binary and `y_score` is 2d. #22284 by Thomas Fan.

- Enhancement `metrics.roc_auc_score` now supports `average=None` in the multiclass case when `multiclass='ovr'` which will return the score per class. #19158 by Nicki Skafte.

- Enhancement Adds `im_kw` parameter to `metrics.ConfusionMatrixDisplay.from_estimator` `metrics.ConfusionMatrixDisplay.from_predictions`, and `metrics.ConfusionMatrixDisplay.plot`. The `im_kw` parameter is passed to the `matplotlib.pyplot.imshow` call when plotting the confusion matrix. #20753 by Thomas Fan.

- Fix `metrics.silhouette_score` now supports integer input for precomputed distances. #22108 by Thomas Fan.

- Fix Fixed a bug in `metrics.normalized_mutual_info_score` which could return unbounded values. #22635 by Jérémie du Boisberranger.

- Fix Fixes `metrics.precision_recall_curve` and `metrics.average_precision_score` when true labels are all negative. #19085 by Varun Agrawal.

- API Change `metrics.SCORERS` is now deprecated and will be removed in 1.3. Please use `metrics.get_scorer_names` to retrieve the names of all available scorers. #22866 by Adrin Jalali.

- API Change Parameters `sample_weight` and `multioutput` of `metrics.mean_absolute_percentage_error` are now keyword-only, in accordance with SLEP009. A deprecation cycle was introduced. #21576 by Paul-Emile Dugnat.

- API Change The `"wminkowski"` metric of `metrics.DistanceMetric` is deprecated and will be removed in version 1.3. Instead the existing `"minkowski"` metric now takes in an optional `w` parameter for weights. This deprecation aims at remaining consistent with SciPy 1.8 convention. #21873 by Yar Khine Phyo.

- API Change `metrics.DistanceMetric` has been moved from `sklearn.neighbors` to `sklearn.metrics`. Using `neighbors.DistanceMetric` for imports is still valid for backward compatibility, but this alias will be removed in 1.3. #21177 by Julien Jerphanion.

### `sklearn.mixture`

- Enhancement `mixture.GaussianMixture` and `mixture.BayesianGaussianMixture` can now be initialized using k-means++ and random data points. #20408 by Gordon Walsh, Alberto Ceballos and Andres Rios.

- Fix Fix a bug that correctly initialize `precisions_cholesky_` in `mixture.GaussianMixture` when providing `precisions_init` by taking its square root. #22058 by Guillaume Lemaitre.

- Fix `mixture.GaussianMixture` now normalizes `weights_` more safely, preventing rounding errors when calling `mixture.GaussianMixture.sample` with `n_components=1`. #23034 by Meekail Zain.

### `sklearn.model_selection`

- Enhancement it is now possible to pass `scoring="matthews_corrcoef"` to all model selection tools with a `scoring` argument to use the Matthews correlation coefficient (MCC). #22203 by Olivier Grisel.

- Enhancement raise an error during cross-validation when the fits for all the splits failed. Similarly raise an error during grid-search when the fits for all the models and all the splits failed. #21026 by Loïc Estève.

- Fix `model_selection.GridSearchCV`, `model_selection.HalvingGridSearchCV` now validate input parameters in `fit` instead of `__init__`. #21880 by Mrinal Tyagi.

- Fix `model_selection.learning_curve` now supports `partial_fit` with regressors. #22982 by Thomas Fan.

### `sklearn.multiclass`

- Enhancement `multiclass.OneVsRestClassifier` now supports a `verbose` parameter so progress on fitting can be seen. #22508 by Chris Combs.

- Fix `multiclass.OneVsOneClassifier.predict` returns correct predictions when the inner classifier only has a predict_proba. #22604 by Thomas Fan.

### `sklearn.neighbors`

- Enhancement Adds get_feature_names_out to `neighbors.RadiusNeighborsTransformer`, `neighbors.KNeighborsTransformer` and `neighbors.NeighborhoodComponentsAnalysis`. #22212 by Meekail Zain.

- Fix `neighbors.KernelDensity` now validates input parameters in `fit` instead of `__init__`. #21430 by Desislava Vasileva and Lucy Jimenez.

- Fix `neighbors.KNeighborsRegressor.predict` now works properly when given an array-like input if `KNeighborsRegressor` is first constructed with a callable passed to the `weights` parameter. #22687 by Meekail Zain.

### `sklearn.neural_network`

- Enhancement `neural_network.MLPClassifier` and `neural_network.MLPRegressor` show error messages when optimizers produce non-finite parameter weights. #22150 by Christian Ritter and Norbert Preining.

- Enhancement Adds get_feature_names_out to `neural_network.BernoulliRBM`. #22248 by Thomas Fan.

### `sklearn.pipeline`

- Enhancement Added support for “passthrough” in `pipeline.FeatureUnion`. Setting a transformer to “passthrough” will pass the features unchanged. #20860 by Shubhraneel Pal.

- Fix `pipeline.Pipeline` now does not validate hyper-parameters in `__init__` but in `.fit()`. #21888 by iofall and Arisa Y..

- Fix `pipeline.FeatureUnion` does not validate hyper-parameters in `__init__`. Validation is now handled in `.fit()` and `.fit_transform()`. #21954 by iofall and Arisa Y..

- Fix Defines `__sklearn_is_fitted__` in `pipeline.FeatureUnion` to return correct result with `utils.validation.check_is_fitted`. #22953 by randomgeek78.

### `sklearn.preprocessing`

- Feature `preprocessing.OneHotEncoder` now supports grouping infrequent categories into a single feature. Grouping infrequent categories is enabled by specifying how to select infrequent categories with `min_frequency` or `max_categories`. #16018 by Thomas Fan.

- Enhancement Adds a `subsample` parameter to `preprocessing.KBinsDiscretizer`. This allows specifying a maximum number of samples to be used while fitting the model. The option is only available when `strategy` is set to `quantile`. #21445 by Felipe Bidu and Amanda Dsouza.

- Enhancement Adds `encoded_missing_value` to `preprocessing.OrdinalEncoder` to configure the encoded value for missing data. #21988 by Thomas Fan.

- Enhancement Added the `get_feature_names_out` method and a new parameter `feature_names_out` to `preprocessing.FunctionTransformer`. You can set `feature_names_out` to ‘one-to-one’ to use the input features names as the output feature names, or you can set it to a callable that returns the output feature names. This is especially useful when the transformer changes the number of features. If `feature_names_out` is None (which is the default), then `get_output_feature_names` is not defined. #21569 by Aurélien Geron.

- Enhancement Adds get_feature_names_out to `preprocessing.Normalizer`, `preprocessing.KernelCenterer`, `preprocessing.OrdinalEncoder`, and `preprocessing.Binarizer`. #21079 by Thomas Fan.

- Fix `preprocessing.PowerTransformer` with `method='yeo-johnson'` better supports significantly non-Gaussian data when searching for an optimal lambda. #20653 by Thomas Fan.

- Fix `preprocessing.LabelBinarizer` now validates input parameters in `fit` instead of `__init__`. #21434 by Krum Arnaudov.

- Fix `preprocessing.FunctionTransformer` with `check_inverse=True` now provides informative error message when input has mixed dtypes. #19916 by Zhehao Liu.

- Fix `preprocessing.KBinsDiscretizer` handles bin edges more consistently now. #14975 by Andreas Müller and #22526 by Meekail Zain.

- Fix Adds `preprocessing.KBinsDiscretizer.get_feature_names_out` support when `encode="ordinal"`. #22735 by Thomas Fan.

### `sklearn.random_projection`

- Enhancement Adds an `inverse_transform` method and a `compute_inverse_transform` parameter to `random_projection.GaussianRandomProjection` and `random_projection.SparseRandomProjection`. When the parameter is set to True, the pseudo-inverse of the components is computed during `fit` and stored as `inverse_components_`. #21701 by Aurélien Geron.

- Enhancement `random_projection.SparseRandomProjection` and `random_projection.GaussianRandomProjection` preserves dtype for `numpy.float32`. #22114 by Takeshi Oura.

- Enhancement Adds get_feature_names_out to all transformers in the `sklearn.random_projection` module: `random_projection.GaussianRandomProjection` and `random_projection.SparseRandomProjection`. #21330 by Loïc Estève.

### `sklearn.svm`

- Enhancement `svm.OneClassSVM`, `svm.NuSVC`, `svm.NuSVR`, `svm.SVC` and `svm.SVR` now expose `n_iter_`, the number of iterations of the libsvm optimization routine. #21408 by Juan Martín Loyola.

- Enhancement `svm.SVR`, `svm.SVC`, `svm.NuSVR`, `svm.OneClassSVM`, `svm.NuSVC` now raise an error when the dual-gap estimation produce non-finite parameter weights. #22149 by Christian Ritter and Norbert Preining.

- Fix `svm.NuSVC`, `svm.NuSVR`, `svm.SVC`, `svm.SVR`, `svm.OneClassSVM` now validate input parameters in `fit` instead of `__init__`. #21436 by Haidar Almubarak.

### `sklearn.tree`

- Enhancement `tree.DecisionTreeClassifier` and `tree.ExtraTreeClassifier` have the new `criterion="log_loss"`, which is equivalent to `criterion="entropy"`. #23047 by Christian Lorentzen.

- Fix Fix a bug in the Poisson splitting criterion for `tree.DecisionTreeRegressor`. #22191 by Christian Lorentzen.

- API Change Changed the default value of `max_features` to 1.0 for `tree.ExtraTreeRegressor` and to `"sqrt"` for `tree.ExtraTreeClassifier`, which will not change the fit result. The original default value `"auto"` has been deprecated and will be removed in version 1.3. Setting `max_features` to `"auto"` is also deprecated for `tree.DecisionTreeClassifier` and `tree.DecisionTreeRegressor`. #22476 by Zhehao Liu.

### `sklearn.utils`

- Enhancement `utils.check_array` and `utils.multiclass.type_of_target` now accept an `input_name` parameter to make the error message more informative when passed invalid input data (e.g. with NaN or infinite values). #21219 by Olivier Grisel.

- Enhancement `utils.check_array` returns a float ndarray with `np.nan` when passed a `Float32` or `Float64` pandas extension array with `pd.NA`. #21278 by Thomas Fan.

- Enhancement `utils.estimator_html_repr` shows a more helpful error message when running in a jupyter notebook that is not trusted. #21316 by Thomas Fan.

- Enhancement `utils.estimator_html_repr` displays an arrow on the top left corner of the HTML representation to show how the elements are clickable. #21298 by Thomas Fan.

- Enhancement `utils.check_array` with `dtype=None` returns numeric arrays when passed in a pandas DataFrame with mixed dtypes. `dtype="numeric"` will also make better infer the dtype when the DataFrame has mixed dtypes. #22237 by Thomas Fan.

- Enhancement `utils.check_scalar` now has better messages when displaying the type. #22218 by Thomas Fan.

- Fix Changes the error message of the `ValidationError` raised by `utils.check_X_y` when y is None so that it is compatible with the `check_requires_y_none` estimator check. #22578 by Claudio Salvatore Arcidiacono.

- Fix `utils.class_weight.compute_class_weight` now only requires that all classes in `y` have a weight in `class_weight`. An error is still raised when a class is present in `y` but not in `class_weight`. #22595 by Thomas Fan.

- Fix `utils.estimator_html_repr` has an improved visualization for nested meta-estimators. #21310 by Thomas Fan.

- Fix `utils.check_scalar` raises an error when `include_boundaries={"left", "right"}` and the boundaries are not set. #22027 by Marie Lanternier.

- Fix `utils.metaestimators.available_if` correctly returns a bounded method that can be pickled. #23077 by Thomas Fan.

- API Change `utils.estimator_checks.check_estimator`’s argument is now called `estimator` (previous name was `Estimator`). #22188 by Mathurin Massias.

- API Change `utils.metaestimators.if_delegate_has_method` is deprecated and will be removed in version 1.3. Use `utils.metaestimators.available_if` instead. #22830 by Jérémie du Boisberranger.

## Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.0, including:

2357juan, Abhishek Gupta, adamgonzo, Adam Li, adijohar, Aditya Kumawat, Aditya Raghuwanshi, Aditya Singh, Adrian Trujillo Duron, Adrin Jalali, ahmadjubair33, AJ Druck, aj-white, Alan Peixinho, Alberto Mario Ceballos-Arroyo, Alek Lefebvre, Alex, Alexandr, Alexandre Gramfort, alexanmv, almeidayoel, Amanda Dsouza, Aman Sharma, Amar pratap singh, Amit, amrcode, András Simon, Andreas Grivas, Andreas Mueller, Andrew Knyazev, Andriy, Angus L’Herrou, Ankit Sharma, Anne Ducout, Arisa, Arth, arthurmello, Arturo Amor, ArturoAmor, Atharva Patil, aufarkari, Aurélien Geron, avm19, Ayan Bag, baam, Bardiya Ak, Behrouz B, Ben3940, Benjamin Bossan, Bharat Raghunathan, Bijil Subhash, bmreiniger, Brandon Truth, Brenden Kadota, Brian Sun, cdrig, Chalmer Lowe, Chiara Marmo, Chitteti Srinath Reddy, Chloe-Agathe Azencott, Christian Lorentzen, Christian Ritter, christopherlim98, Christoph T. Weidemann, Christos Aridas, Claudio Salvatore Arcidiacono, combscCode, Daniela Fernandes, darioka, Darren Nguyen, Dave Eargle, David Gilbertson, David Poznik, Dea María Léon, Dennis Osei, DessyVV, Dev514, Dimitri Papadopoulos Orfanos, Diwakar Gupta, Dr. Felix M. Riese, drskd, Emiko Sano, Emmanouil Gionanidis, EricEllwanger, Erich Schubert, Eric Larson, Eric Ndirangu, ErmolaevPA, Estefania Barreto-Ojeda, eyast, Fatima GASMI, Federico Luna, Felix Glushchenkov, fkaren27, Fortune Uwha, FPGAwesome, francoisgoupil, Frans Larsson, ftorres16, Gabor Berei, Gabor Kertesz, Gabriel Stefanini Vicente, Gabriel S Vicente, Gael Varoquaux, GAURAV CHOUDHARY, Gauthier I, genvalen, Geoffrey-Paris, Giancarlo Pablo, glennfrutiz, gpapadok, Guillaume Lemaitre, Guillermo Tomás Fernández Martín, Gustavo Oliveira, Haidar Almubarak, Hannah Bohle, Hansin Ahuja, Haoyin Xu, Haya, Helder Geovane Gomes de Lima, henrymooresc, Hideaki Imamura, Himanshu Kumar, Hind-M, hmasdev, hvassard, i-aki-y, iasoon, Inclusive Coding Bot, Ingela, iofall, Ishan Kumar, Jack Liu, Jake Cowton, jalexand3r, J Alexander, Jauhar, Jaya Surya Kommireddy, Jay Stanley, Jeff Hale, je-kr, JElfner, Jenny Vo, Jérémie du Boisberranger, Jihane, Jirka Borovec, Joel Nothman, Jon Haitz Legarreta Gorroño, Jordan Silke, Jorge Ciprián, Jorge Loayza, Joseph Chazalon, Joseph Schwartz-Messing, Jovan Stojanovic, JSchuerz, Juan Carlos Alfaro Jiménez, Juan Martin Loyola, Julien Jerphanion, katotten, Kaushik Roy Chowdhury, Ken4git, Kenneth Prabakaran, kernc, Kevin Doucet, KimAYoung, Koushik Joshi, Kranthi Sedamaki, krishna kumar, krumetoft, lesnee, Lisa Casino, Logan Thomas, Loic Esteve, Louis Wagner, LucieClair, Lucy Liu, Luiz Eduardo Amaral, Magali, MaggieChege, Mai, mandjevant, Mandy Gu, Manimaran, MarcoM, Marco Wurps, Maren Westermann, Maria Boerner, MarieS-WiMLDS, Martel Corentin, martin-kokos, mathurinm, Matías, matjansen, Matteo Francia, Maxwell, Meekail Zain, Megabyte, Mehrdad Moradizadeh, melemo2, Michael I Chen, michalkrawczyk, Micky774, milana2, millawell, Ming-Yang Ho, Mitzi, miwojc, Mizuki, mlant, Mohamed Haseeb, Mohit Sharma, Moonkyung94, mpoemsl, MrinalTyagi, Mr. Leu, msabatier, murata-yu, N, Nadirhan Şahin, Naipawat Poolsawat, NartayXD, nastegiano, nathansquan, nat-salt, Nicki Skafte Detlefsen, Nicolas Hug, Niket Jain, Nikhil Suresh, Nikita Titov, Nikolay Kondratyev, Ohad Michel, Oleksandr Husak, Olivier Grisel, partev, Patrick Ferreira, Paul, pelennor, PierreAttard, Piet Brömmel, Pieter Gijsbers, Pinky, poloso, Pramod Anantharam, puhuk, Purna Chandra Mansingh, QuadV, Rahil Parikh, Randall Boyes, randomgeek78, Raz Hoshia, Reshama Shaikh, Ricardo Ferreira, Richard Taylor, Rileran, Rishabh, Robin Thibaut, Rocco Meli, Roman Feldbauer, Roman Yurchak, Ross Barnowski, rsnegrin, Sachin Yadav, sakinaOuisrani, Sam Adam Day, Sanjay Marreddi, Sebastian Pujalte, SEELE, SELEE, Seyedsaman (Sam) Emami, ShanDeng123, Shao Yang Hong, sharmadharmpal, shaymerNaturalint, Shuangchi He, Shubhraneel Pal, siavrez, slishak, Smile, spikebh, sply88, Srinath Kailasa, Stéphane Collot, Sultan Orazbayev, Sumit Saha, Sven Eschlbeck, Sven Stehle, Swapnil Jha, Sylvain Marié, Takeshi Oura, Tamires Santana, Tenavi, teunpe, Theis Ferré Hjortkjær, Thiruvenkadam, Thomas J. Fan, t-jakubek, toastedyeast, Tom Dupré la Tour, Tom McTiernan, TONY GEORGE, Tyler Martin, Tyler Reddy, Udit Gupta, Ugo Marchand, Varun Agrawal, Venkatachalam N, Vera Komeyer, victoirelouis, Vikas Vishwakarma, Vikrant khedkar, Vladimir Chernyy, Vladimir Kim, WeijiaDu, Xiao Yuan, Yar Khine Phyo, Ying Xiong, yiyangq, Yosshi999, Yuki Koyama, Zach Deane-Mayer, Zeel B Patel, zempleni, zhenfisher, 赵丰 (Zhao Feng)