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# Version 1.2.2

March 2023

## Changelog

### `sklearn.base`

- Fix When `set_output(transform="pandas")`, `base.TransformerMixin` maintains the index if the transform output is already a DataFrame. #25747 by Thomas Fan.

### `sklearn.calibration`

- Fix A deprecation warning is raised when using the `base_estimator__` prefix to set parameters of the estimator used in `calibration.CalibratedClassifierCV`. #25477 by Tim Head.

### `sklearn.cluster`

- Fix Fixed a bug in `cluster.BisectingKMeans`, preventing `fit` to randomly fail due to a permutation of the labels when running multiple inits. #25563 by Jérémie du Boisberranger.

### `sklearn.compose`

- Fix Fixes a bug in `compose.ColumnTransformer` which now supports empty selection of columns when `set_output(transform="pandas")`. #25570 by Thomas Fan.

### `sklearn.ensemble`

- Fix A deprecation warning is raised when using the `base_estimator__` prefix to set parameters of the estimator used in `ensemble.AdaBoostClassifier`, `ensemble.AdaBoostRegressor`, `ensemble.BaggingClassifier`, and `ensemble.BaggingRegressor`. #25477 by Tim Head.

### `sklearn.feature_selection`

- Fix Fixed a regression where a negative `tol` would not be accepted any more by `feature_selection.SequentialFeatureSelector`. #25664 by Jérémie du Boisberranger.

### `sklearn.inspection`

- Fix Raise a more informative error message in `inspection.partial_dependence` when dealing with mixed data type categories that cannot be sorted by `numpy.unique`. This problem usually happen when categories are `str` and missing values are present using `np.nan`. #25774 by Guillaume Lemaitre.

### `sklearn.isotonic`

- Fix Fixes a bug in `isotonic.IsotonicRegression` where `isotonic.IsotonicRegression.predict` would return a pandas DataFrame when the global configuration sets `transform_output="pandas"`. #25500 by Guillaume Lemaitre.

### `sklearn.preprocessing`

- Fix `preprocessing.OneHotEncoder.drop_idx_` now properly references the dropped category in the `categories_` attribute when there are infrequent categories. #25589 by Thomas Fan.

- Fix `preprocessing.OrdinalEncoder` now correctly supports `encoded_missing_value` or `unknown_value` set to a categories’ cardinality when there is missing values in the training data. #25704 by Thomas Fan.

### `sklearn.tree`

- Fix Fixed a regression in `tree.DecisionTreeClassifier`, `tree.DecisionTreeRegressor`, `tree.ExtraTreeClassifier` and `tree.ExtraTreeRegressor` where an error was no longer raised in version 1.2 when `min_sample_split=1`. #25744 by Jérémie du Boisberranger.

### `sklearn.utils`

- Fix Fixes a bug in `utils.check_array` which now correctly performs non-finite validation with the Array API specification. #25619 by Thomas Fan.

- Fix `utils.multiclass.type_of_target` can identify pandas nullable data types as classification targets. #25638 by Thomas Fan.

# Version 1.2.1

January 2023

## 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 The fitted components in `decomposition.MiniBatchDictionaryLearning` might differ. The online updates of the sufficient statistics now properly take the sizes of the batches into account. #25354 by Jérémie du Boisberranger.

- Fix The `categories_` attribute of `preprocessing.OneHotEncoder` now always contains an array of `object`s when using predefined categories that are strings. Predefined categories encoded as bytes will no longer work with `X` encoded as strings. #25174 by Tim Head.

## Changes impacting all modules

- Fix Support `pandas.Int64` dtyped `y` for classifiers and regressors. #25089 by Tim Head.

- Fix Remove spurious warnings for estimators internally using neighbors search methods. #25129 by Julien Jerphanion.

- Fix Fix a bug where the current configuration was ignored in estimators using `n_jobs > 1`. This bug was triggered for tasks dispatched by the auxiliary thread of `joblib` as `sklearn.get_config` used to access an empty thread local configuration instead of the configuration visible from the thread where `joblib.Parallel` was first called. #25363 by Guillaume Lemaitre.

## Changelog

### `sklearn.base`

- Fix Fix a regression in `BaseEstimator.__getstate__` that would prevent certain estimators to be pickled when using Python 3.11. #25188 by Benjamin Bossan.

- Fix Inheriting from `base.TransformerMixin` will only wrap the `transform` method if the class defines `transform` itself. #25295 by Thomas Fan.

### `sklearn.datasets`

- Fix Fixes an inconsistency in `datasets.fetch_openml` between liac-arff and pandas parser when a leading space is introduced after the delimiter. The ARFF specs requires to ignore the leading space. #25312 by Guillaume Lemaitre.

- Fix Fixes a bug in `datasets.fetch_openml` when using `parser="pandas"` where single quote and backslash escape characters were not properly handled. #25511 by Guillaume Lemaitre.

### `sklearn.decomposition`

- Fix Fixed a bug in `decomposition.MiniBatchDictionaryLearning` where the online updates of the sufficient statistics where not correct when calling `partial_fit` on batches of different sizes. #25354 by Jérémie du Boisberranger.

- Fix `decomposition.DictionaryLearning` better supports readonly NumPy arrays. In particular, it better supports large datasets which are memory-mapped when it is used with coordinate descent algorithms (i.e. when `fit_algorithm='cd'`). #25172 by Julien Jerphanion.

### `sklearn.ensemble`

- Fix `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor` `ensemble.ExtraTreesClassifier` and `ensemble.ExtraTreesRegressor` now support sparse readonly datasets. #25341 by Julien Jerphanion

### `sklearn.feature_extraction`

- Fix `feature_extraction.FeatureHasher` raises an informative error when the input is a list of strings. #25094 by Thomas Fan.

### `sklearn.linear_model`

- Fix Fix a regression in `linear_model.SGDClassifier` and `linear_model.SGDRegressor` that makes them unusable with the `verbose` parameter set to a value greater than 0. #25250 by Jérémie Du Boisberranger.

### `sklearn.manifold`

- Fix `manifold.TSNE` now works correctly when output type is set to pandas #25370 by Tim Head.

### `sklearn.model_selection`

- Fix `model_selection.cross_validate` with multimetric scoring in case of some failing scorers the non-failing scorers now returns proper scores instead of `error_score` values. #23101 by András Simon and Thomas Fan.

### `sklearn.neural_network`

- Fix `neural_network.MLPClassifier` and `neural_network.MLPRegressor` no longer raise warnings when fitting data with feature names. #24873 by Tim Head.

- Fix Improves error message in `neural_network.MLPClassifier` and `neural_network.MLPRegressor`, when `early_stopping=True` and `partial_fit` is called. #25694 by Thomas Fan.

### `sklearn.preprocessing`

- Fix `preprocessing.FunctionTransformer.inverse_transform` correctly supports DataFrames that are all numerical when `check_inverse=True`. #25274 by Thomas Fan.

- Fix `preprocessing.SplineTransformer.get_feature_names_out` correctly returns feature names when `extrapolations="periodic"`. #25296 by Thomas Fan.

### `sklearn.tree`

- Fix `tree.DecisionTreeClassifier`, `tree.DecisionTreeRegressor` `tree.ExtraTreeClassifier` and `tree.ExtraTreeRegressor` now support sparse readonly datasets. #25341 by Julien Jerphanion

### `sklearn.utils`

- Fix Restore `utils.check_array`’s behaviour for pandas Series of type boolean. The type is maintained, instead of converting to `float64.` #25147 by Tim Head.

- API Change `utils.fixes.delayed` is deprecated in 1.2.1 and will be removed in 1.5. Instead, import `utils.parallel.delayed` and use it in conjunction with the newly introduced `utils.parallel.Parallel` to ensure proper propagation of the scikit-learn configuration to the workers. #25363 by Guillaume Lemaitre.

# Version 1.2.0

December 2022

For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.2.

## 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.

## 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.

- Enhancement The default `eigen_tol` for `cluster.SpectralClustering`, `manifold.SpectralEmbedding`, `cluster.spectral_clustering`, and `manifold.spectral_embedding` is now `None` when using the `'amg'` or `'lobpcg'` solvers. This change improves numerical stability of the solver, but may result in a different model.

- Enhancement `linear_model.GammaRegressor`, `linear_model.PoissonRegressor` and `linear_model.TweedieRegressor` can reach higher precision with the lbfgs solver, in particular when `tol` is set to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B. #23619 by Christian Lorentzen.

- Enhancement The default value for `eps` `metrics.log_loss` has changed from `1e-15` to `"auto"`. `"auto"` sets `eps` to `np.finfo(y_pred.dtype).eps`. #24354 by Safiuddin Khaja and gsiisg.

- Fix Make sign of `components_` deterministic in `decomposition.SparsePCA`. #23935 by Guillaume Lemaitre.

- Fix The `components_` signs in `decomposition.FastICA` might differ. It is now consistent and deterministic with all SVD solvers. #22527 by Meekail Zain and Thomas Fan.

- Fix The condition for early stopping has now been changed in `linear_model._sgd_fast._plain_sgd` which is used by `linear_model.SGDRegressor` and `linear_model.SGDClassifier`. The old condition did not disambiguate between training and validation set and had an effect of overscaling the error tolerance. This has been fixed in #23798 by Harsh Agrawal.

- Fix For `model_selection.GridSearchCV` and `model_selection.RandomizedSearchCV` ranks corresponding to nan scores will all be set to the maximum possible rank. #24543 by Guillaume Lemaitre.

- API Change The default value of `tol` was changed from `1e-3` to `1e-4` for `linear_model.ridge_regression`, `linear_model.Ridge` and `linear_model.RidgeClassifier`. #24465 by Christian Lorentzen.

## Changes impacting all modules

- Major Feature The `set_output` API has been adopted by all transformers. Meta-estimators that contain transformers such as `pipeline.Pipeline` or `compose.ColumnTransformer` also define a `set_output`. For details, see SLEP018. #23734 and #24699 by Thomas Fan.

- Efficiency Low-level routines for reductions on pairwise distances for dense float32 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.

  #23865 by Julien Jerphanion.

- Enhancement Finiteness checks (detection of NaN and infinite values) in all estimators are now significantly more efficient for float32 data by leveraging NumPy’s SIMD optimized primitives. #23446 by Meekail Zain

- Enhancement Finiteness checks (detection of NaN and infinite values) in all estimators are now faster by utilizing a more efficient stop-on-first second-pass algorithm. #23197 by Meekail Zain

- Enhancement Support for combinations of dense and sparse datasets pairs for all distance metrics and for float32 and float64 datasets has been added or has seen its performance improved for the following estimators:

  - `sklearn.metrics.pairwise_distances_argmin`

  - `sklearn.metrics.pairwise_distances_argmin_min`

  - `sklearn.cluster.AffinityPropagation`

  - `sklearn.cluster.Birch`

  - `sklearn.cluster.SpectralClustering`

  - `sklearn.neighbors.KNeighborsClassifier`

  - `sklearn.neighbors.KNeighborsRegressor`

  - `sklearn.neighbors.RadiusNeighborsClassifier`

  - `sklearn.neighbors.RadiusNeighborsRegressor`

  - `sklearn.neighbors.LocalOutlierFactor`

  - `sklearn.neighbors.NearestNeighbors`

  - `sklearn.manifold.Isomap`

  - `sklearn.manifold.TSNE`

  - `sklearn.manifold.trustworthiness`

  #23604 and #23585 by Julien Jerphanion, Olivier Grisel, and Thomas Fan, #24556 by Vincent Maladière.

- Fix Systematically check the sha256 digest of dataset tarballs used in code examples in the documentation. #24617 by Olivier Grisel and Thomas Fan. Thanks to Sim4n6 for the report.

## Changelog

### `sklearn.base`

- Enhancement Introduces `base.ClassNamePrefixFeaturesOutMixin` and `base.ClassNamePrefixFeaturesOutMixin` mixins that defines get_feature_names_out for common transformer uses cases. #24688 by Thomas Fan.

### `sklearn.calibration`

- API Change Rename `base_estimator` to `estimator` in `calibration.CalibratedClassifierCV` to improve readability and consistency. The parameter `base_estimator` is deprecated and will be removed in 1.4. #22054 by Kevin Roice.

### `sklearn.cluster`

- Efficiency `cluster.KMeans` with `algorithm="lloyd"` is now faster and uses less memory. #24264 by Vincent Maladiere.

- Enhancement The `predict` and `fit_predict` methods of `cluster.OPTICS` now accept sparse data type for input data. #14736 by Hunt Zhan, #20802 by Brandon Pokorny, and #22965 by Meekail Zain.

- Enhancement `cluster.Birch` now preserves dtype for `numpy.float32` inputs. #22968 by `Meekail Zain <micky774>`.

- Enhancement `cluster.KMeans` and `cluster.MiniBatchKMeans` now accept a new `'auto'` option for `n_init` which changes the number of random initializations to one when using `init='k-means++'` for efficiency. This begins deprecation for the default values of `n_init` in the two classes and both will have their defaults changed to `n_init='auto'` in 1.4. #23038 by Meekail Zain.

- Enhancement `cluster.SpectralClustering` and `cluster.spectral_clustering` now propagates the `eigen_tol` parameter to all choices of `eigen_solver`. Includes a new option `eigen_tol="auto"` and begins deprecation to change the default from `eigen_tol=0` to `eigen_tol="auto"` in version 1.3. #23210 by Meekail Zain.

- Fix `cluster.KMeans` now supports readonly attributes when predicting. #24258 by Thomas Fan

- API Change The `affinity` attribute is now deprecated for `cluster.AgglomerativeClustering` and will be renamed to `metric` in v1.4. #23470 by Meekail Zain.

### `sklearn.datasets`

- Enhancement Introduce the new parameter `parser` in `datasets.fetch_openml`. `parser="pandas"` allows to use the very CPU and memory efficient `pandas.read_csv` parser to load dense ARFF formatted dataset files. It is possible to pass `parser="liac-arff"` to use the old LIAC parser. When `parser="auto"`, dense datasets are loaded with “pandas” and sparse datasets are loaded with “liac-arff”. Currently, `parser="liac-arff"` by default and will change to `parser="auto"` in version 1.4 #21938 by Guillaume Lemaitre.

- Enhancement `datasets.dump_svmlight_file` is now accelerated with a Cython implementation, providing 2-4x speedups. #23127 by Meekail Zain

- Enhancement Path-like objects, such as those created with pathlib are now allowed as paths in `datasets.load_svmlight_file` and `datasets.load_svmlight_files`. #19075 by Carlos Ramos Carreño.

- Fix Make sure that `datasets.fetch_lfw_people` and `datasets.fetch_lfw_pairs` internally crops images based on the `slice_` parameter. #24951 by Guillaume Lemaitre.

### `sklearn.decomposition`

- Efficiency `decomposition.FastICA.fit` has been optimised w.r.t its memory footprint and runtime. #22268 by MohamedBsh.

- Enhancement `decomposition.SparsePCA` and `decomposition.MiniBatchSparsePCA` now implements an `inverse_transform` function. #23905 by Guillaume Lemaitre.

- Enhancement `decomposition.FastICA` now allows the user to select how whitening is performed through the new `whiten_solver` parameter, which supports `svd` and `eigh`. `whiten_solver` defaults to `svd` although `eigh` may be faster and more memory efficient in cases where `num_features > num_samples`. #11860 by Pierre Ablin, #22527 by Meekail Zain and Thomas Fan.

- Enhancement `decomposition.LatentDirichletAllocation` now preserves dtype for `numpy.float32` input. #24528 by Takeshi Oura and Jérémie du Boisberranger.

- Fix Make sign of `components_` deterministic in `decomposition.SparsePCA`. #23935 by Guillaume Lemaitre.

- API Change The `n_iter` parameter of `decomposition.MiniBatchSparsePCA` is deprecated and replaced by the parameters `max_iter`, `tol`, and `max_no_improvement` to be consistent with `decomposition.MiniBatchDictionaryLearning`. `n_iter` will be removed in version 1.3. #23726 by Guillaume Lemaitre.

- API Change The `n_features_` attribute of `decomposition.PCA` is deprecated in favor of `n_features_in_` and will be removed in 1.4. #24421 by Kshitij Mathur.

### `sklearn.discriminant_analysis`

- Major Feature `discriminant_analysis.LinearDiscriminantAnalysis` now supports the Array API for `solver="svd"`. Array API support is considered experimental and might evolve without being subjected to our usual rolling deprecation cycle policy. See Array API support (experimental) for more details. #22554 by Thomas Fan.

- Fix Validate parameters only in `fit` and not in `__init__` for `discriminant_analysis.QuadraticDiscriminantAnalysis`. #24218 by Stefanie Molin.

### `sklearn.ensemble`

- Major Feature `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` now support interaction constraints via the argument `interaction_cst` of their constructors. #21020 by Christian Lorentzen. Using interaction constraints also makes fitting faster. #24856 by Christian Lorentzen.

- Feature Adds `class_weight` to `ensemble.HistGradientBoostingClassifier`. #22014 by Thomas Fan.

- Efficiency Improve runtime performance of `ensemble.IsolationForest` by avoiding data copies. #23252 by Zhehao Liu.

- Enhancement `ensemble.StackingClassifier` now accepts any kind of base estimator. #24538 by Guillem G Subies.

- Enhancement Make it possible to pass the `categorical_features` parameter of `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` as feature names. #24889 by Olivier Grisel.

- Enhancement `ensemble.StackingClassifier` now supports multilabel-indicator target #24146 by Nicolas Peretti, Nestor Navarro, Nati Tomattis, and Vincent Maladiere.

- Enhancement `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingClassifier` now accept their `monotonic_cst` parameter to be passed as a dictionary in addition to the previously supported array-like format. Such dictionary have feature names as keys and one of `-1`, `0`, `1` as value to specify monotonicity constraints for each feature. #24855 by Olivier Grisel.

- Enhancement Interaction constraints for `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` can now be specified as strings for two common cases: “no_interactions” and “pairwise” interactions. #24849 by Tim Head.

- Fix Fixed the issue where `ensemble.AdaBoostClassifier` outputs NaN in feature importance when fitted with very small sample weight. #20415 by Zhehao Liu.

- Fix `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` no longer error when predicting on categories encoded as negative values and instead consider them a member of the “missing category”. #24283 by Thomas Fan.

- Fix `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor`, with `verbose>=1`, print detailed timing information on computing histograms and finding best splits. The time spent in the root node was previously missing and is now included in the printed information. #24894 by Christian Lorentzen.

- API Change Rename the constructor parameter `base_estimator` to `estimator` in the following classes: `ensemble.BaggingClassifier`, `ensemble.BaggingRegressor`, `ensemble.AdaBoostClassifier`, `ensemble.AdaBoostRegressor`. `base_estimator` is deprecated in 1.2 and will be removed in 1.4. #23819 by Adrian Trujillo and Edoardo Abati.

- API Change Rename the fitted attribute `base_estimator_` to `estimator_` in the following classes: `ensemble.BaggingClassifier`, `ensemble.BaggingRegressor`, `ensemble.AdaBoostClassifier`, `ensemble.AdaBoostRegressor`, `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor`, `ensemble.ExtraTreesClassifier`, `ensemble.ExtraTreesRegressor`, `ensemble.RandomTreesEmbedding`, `ensemble.IsolationForest`. `base_estimator_` is deprecated in 1.2 and will be removed in 1.4. #23819 by Adrian Trujillo and Edoardo Abati.

### `sklearn.feature_selection`

- Fix Fix a bug in `feature_selection.mutual_info_regression` and `feature_selection.mutual_info_classif`, where the continuous features in `X` should be scaled to a unit variance independently if the target `y` is continuous or discrete. #24747 by Guillaume Lemaitre

### `sklearn.gaussian_process`

- Fix Fix `gaussian_process.kernels.Matern` gradient computation with `nu=0.5` for PyPy (and possibly other non CPython interpreters). #24245 by Loïc Estève.

- Fix The `fit` method of `gaussian_process.GaussianProcessRegressor` will not modify the input X in case a custom kernel is used, with a `diag` method that returns part of the input X. #24405 by Omar Salman.

### `sklearn.impute`

- Enhancement Added `keep_empty_features` parameter to `impute.SimpleImputer`, `impute.KNNImputer` and `impute.IterativeImputer`, preventing removal of features containing only missing values when transforming. #16695 by Vitor Santa Rosa.

### `sklearn.inspection`

- Major Feature Extended `inspection.partial_dependence` and `inspection.PartialDependenceDisplay` to handle categorical features. #18298 by Madhura Jayaratne and Guillaume Lemaitre.

- Fix `inspection.DecisionBoundaryDisplay` now raises error if input data is not 2-dimensional. #25077 by Arturo Amor.

### `sklearn.kernel_approximation`

- Enhancement `kernel_approximation.RBFSampler` now preserves dtype for `numpy.float32` inputs. #24317 by `Tim Head <betatim>`.

- Enhancement `kernel_approximation.SkewedChi2Sampler` now preserves dtype for `numpy.float32` inputs. #24350 by Rahil Parikh.

- Enhancement `kernel_approximation.RBFSampler` now accepts `'scale'` option for parameter `gamma`. #24755 by Gleb Levitski.

### `sklearn.linear_model`

- Enhancement `linear_model.LogisticRegression`, `linear_model.LogisticRegressionCV`, `linear_model.GammaRegressor`, `linear_model.PoissonRegressor` and `linear_model.TweedieRegressor` got a new solver `solver="newton-cholesky"`. This is a 2nd order (Newton) optimisation routine that uses a Cholesky decomposition of the hessian matrix. When `n_samples >> n_features`, the `"newton-cholesky"` solver has been observed to converge both faster and to a higher precision solution than the `"lbfgs"` solver on problems with one-hot encoded categorical variables with some rare categorical levels. #24637 and #24767 by Christian Lorentzen.

- Enhancement `linear_model.GammaRegressor`, `linear_model.PoissonRegressor` and `linear_model.TweedieRegressor` can reach higher precision with the lbfgs solver, in particular when `tol` is set to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B. #23619 by Christian Lorentzen.

- Fix `linear_model.SGDClassifier` and `linear_model.SGDRegressor` will raise an error when all the validation samples have zero sample weight. #23275 by `Zhehao Liu <MaxwellLZH>`.

- Fix `linear_model.SGDOneClassSVM` no longer performs parameter validation in the constructor. All validation is now handled in `fit()` and `partial_fit()`. #24433 by Yogendrasingh, Arisa Y. and Tim Head.

- Fix Fix average loss calculation when early stopping is enabled in `linear_model.SGDRegressor` and `linear_model.SGDClassifier`. Also updated the condition for early stopping accordingly. #23798 by Harsh Agrawal.

- API Change The default value for the `solver` parameter in `linear_model.QuantileRegressor` will change from `"interior-point"` to `"highs"` in version 1.4. #23637 by Guillaume Lemaitre.

- API Change String option `"none"` is deprecated for `penalty` argument in `linear_model.LogisticRegression`, and will be removed in version 1.4. Use `None` instead. #23877 by Zhehao Liu.

- API Change The default value of `tol` was changed from `1e-3` to `1e-4` for `linear_model.ridge_regression`, `linear_model.Ridge` and `linear_model.RidgeClassifier`. #24465 by Christian Lorentzen.

### `sklearn.manifold`

- Feature Adds option to use the normalized stress in `manifold.MDS`. This is enabled by setting the new `normalize` parameter to `True`. #10168 by Łukasz Borchmann, #12285 by Matthias Miltenberger, #13042 by Matthieu Parizy, #18094 by Roth E Conrad and #22562 by Meekail Zain.

- Enhancement Adds `eigen_tol` parameter to `manifold.SpectralEmbedding`. Both `manifold.spectral_embedding` and `manifold.SpectralEmbedding` now propagate `eigen_tol` to all choices of `eigen_solver`. Includes a new option `eigen_tol="auto"` and begins deprecation to change the default from `eigen_tol=0` to `eigen_tol="auto"` in version 1.3. #23210 by Meekail Zain.

- Enhancement `manifold.Isomap` now preserves dtype for `np.float32` inputs. #24714 by Rahil Parikh.

- API Change Added an `"auto"` option to the `normalized_stress` argument in `manifold.MDS` and `manifold.smacof`. Note that `normalized_stress` is only valid for non-metric MDS, therefore the `"auto"` option enables `normalized_stress` when `metric=False` and disables it when `metric=True`. `"auto"` will become the default value for `normalized_stress` in version 1.4. #23834 by Meekail Zain

### `sklearn.metrics`

- Feature `metrics.ConfusionMatrixDisplay.from_estimator`, `metrics.ConfusionMatrixDisplay.from_predictions`, and `metrics.ConfusionMatrixDisplay.plot` accepts a `text_kw` parameter which is passed to matplotlib’s `text` function. #24051 by Thomas Fan.

- Feature `metrics.class_likelihood_ratios` is added to compute the positive and negative likelihood ratios derived from the confusion matrix of a binary classification problem. #22518 by Arturo Amor.

- Feature Add `metrics.PredictionErrorDisplay` to plot residuals vs predicted and actual vs predicted to qualitatively assess the behavior of a regressor. The display can be created with the class methods `metrics.PredictionErrorDisplay.from_estimator` and `metrics.PredictionErrorDisplay.from_predictions`. #18020 by Guillaume Lemaitre.

- Feature `metrics.roc_auc_score` now supports micro-averaging (`average="micro"`) for the One-vs-Rest multiclass case (`multi_class="ovr"`). #24338 by Arturo Amor.

- Enhancement Adds an `"auto"` option to `eps` in `metrics.log_loss`. This option will automatically set the `eps` value depending on the data type of `y_pred`. In addition, the default value of `eps` is changed from `1e-15` to the new `"auto"` option. #24354 by Safiuddin Khaja and gsiisg.

- Fix Allows `csr_matrix` as input for parameter: `y_true` of

    the `metrics.label_ranking_average_precision_score` metric. #23442 by Sean Atukorala

- Fix `metrics.ndcg_score` will now trigger a warning when the `y_true` value contains a negative value. Users may still use negative values, but the result may not be between 0 and 1. Starting in v1.4, passing in negative values for `y_true` will raise an error. #22710 by Conroy Trinh and #23461 by Meekail Zain.

- Fix `metrics.log_loss` with `eps=0` now returns a correct value of 0 or `np.inf` instead of `nan` for predictions at the boundaries (0 or 1). It also accepts integer input. #24365 by Christian Lorentzen.

- API Change The parameter `sum_over_features` of `metrics.pairwise.manhattan_distances` is deprecated and will be removed in 1.4. #24630 by Rushil Desai.

### `sklearn.model_selection`

- Feature Added the class `model_selection.LearningCurveDisplay` that allows to make easy plotting of learning curves obtained by the function `model_selection.learning_curve`. #24084 by Guillaume Lemaitre.

- Fix For all `SearchCV` classes and scipy >= 1.10, rank corresponding to a nan score is correctly set to the maximum possible rank, rather than `np.iinfo(np.int32).min`. #24141 by Loïc Estève.

- Fix In both `model_selection.HalvingGridSearchCV` and `model_selection.HalvingRandomSearchCV` parameter combinations with a NaN score now share the lowest rank. #24539 by Tim Head.

- Fix For `model_selection.GridSearchCV` and `model_selection.RandomizedSearchCV` ranks corresponding to nan scores will all be set to the maximum possible rank. #24543 by Guillaume Lemaitre.

### `sklearn.multioutput`

- Feature Added boolean `verbose` flag to classes: `multioutput.ClassifierChain` and `multioutput.RegressorChain`. #23977 by Eric Fiegel, Chiara Marmo, Lucy Liu, and Guillaume Lemaitre.

### `sklearn.naive_bayes`

- Feature Add methods `predict_joint_log_proba` to all naive Bayes classifiers. #23683 by Andrey Melnik.

- Enhancement A new parameter `force_alpha` was added to `naive_bayes.BernoulliNB`, `naive_bayes.ComplementNB`, `naive_bayes.CategoricalNB`, and `naive_bayes.MultinomialNB`, allowing user to set parameter alpha to a very small number, greater or equal 0, which was earlier automatically changed to `1e-10` instead. #16747 by @arka204, #18805 by @hongshaoyang, #22269 by Meekail Zain.

### `sklearn.neighbors`

- Feature Adds new function `neighbors.sort_graph_by_row_values` to sort a CSR sparse graph such that each row is stored with increasing values. This is useful to improve efficiency when using precomputed sparse distance matrices in a variety of estimators and avoid an `EfficiencyWarning`. #23139 by Tom Dupre la Tour.

- Efficiency `neighbors.NearestCentroid` is faster and requires less memory as it better leverages CPUs’ caches to compute predictions. #24645 by Olivier Grisel.

- Enhancement `neighbors.KernelDensity` bandwidth parameter now accepts definition using Scott’s and Silverman’s estimation methods. #10468 by Ruben and #22993 by Jovan Stojanovic.

- Enhancement `neighbors.NeighborsBase` now accepts Minkowski semi-metric (i.e. when \(0 < p < 1\) for `metric="minkowski"`) for `algorithm="auto"` or `algorithm="brute"`. #24750 by Rudresh Veerkhare

- Fix `neighbors.NearestCentroid` now raises an informative error message at fit-time instead of failing with a low-level error message at predict-time. #23874 by Juan Gomez.

- Fix Set `n_jobs=None` by default (instead of `1`) for `neighbors.KNeighborsTransformer` and `neighbors.RadiusNeighborsTransformer`. #24075 by Valentin Laurent.

- Enhancement `neighbors.LocalOutlierFactor` now preserves dtype for `numpy.float32` inputs. #22665 by Julien Jerphanion.

### `sklearn.neural_network`

- Fix `neural_network.MLPClassifier` and `neural_network.MLPRegressor` always expose the parameters `best_loss_`, `validation_scores_`, and `best_validation_score_`. `best_loss_` is set to `None` when `early_stopping=True`, while `validation_scores_` and `best_validation_score_` are set to `None` when `early_stopping=False`. #24683 by Guillaume Lemaitre.

### `sklearn.pipeline`

- Enhancement `pipeline.FeatureUnion.get_feature_names_out` can now be used when one of the transformers in the `pipeline.FeatureUnion` is `"passthrough"`. #24058 by Diederik Perdok

- Enhancement The `pipeline.FeatureUnion` class now has a `named_transformers` attribute for accessing transformers by name. #20331 by Christopher Flynn.

### `sklearn.preprocessing`

- Enhancement `preprocessing.FunctionTransformer` will always try to set `n_features_in_` and `feature_names_in_` regardless of the `validate` parameter. #23993 by Thomas Fan.

- Fix `preprocessing.LabelEncoder` correctly encodes NaNs in `transform`. #22629 by Thomas Fan.

- API Change The `sparse` parameter of `preprocessing.OneHotEncoder` is now deprecated and will be removed in version 1.4. Use `sparse_output` instead. #24412 by Rushil Desai.

### `sklearn.svm`

- API Change The `class_weight_` attribute is now deprecated for `svm.NuSVR`, `svm.SVR`, `svm.OneClassSVM`. #22898 by Meekail Zain.

### `sklearn.tree`

- Enhancement `tree.plot_tree`, `tree.export_graphviz` now uses a lower case `x[i]` to represent feature `i`. #23480 by Thomas Fan.

### `sklearn.utils`

- Feature A new module exposes development tools to discover estimators (i.e. `utils.discovery.all_estimators`), displays (i.e. `utils.discovery.all_displays`) and functions (i.e. `utils.discovery.all_functions`) in scikit-learn. #21469 by Guillaume Lemaitre.

- Enhancement `utils.extmath.randomized_svd` now accepts an argument, `lapack_svd_driver`, to specify the lapack driver used in the internal deterministic SVD used by the randomized SVD algorithm. #20617 by Srinath Kailasa

- Enhancement `utils.validation.column_or_1d` now accepts a `dtype` parameter to specific `y`’s dtype. #22629 by Thomas Fan.

- Enhancement `utils.extmath.cartesian` now accepts arrays with different `dtype` and will cast the output to the most permissive `dtype`. #25067 by Guillaume Lemaitre.

- Fix `utils.multiclass.type_of_target` now properly handles sparse matrices. #14862 by Léonard Binet.

- Fix HTML representation no longer errors when an estimator class is a value in `get_params`. #24512 by Thomas Fan.

- Fix `utils.estimator_checks.check_estimator` now takes into account the `requires_positive_X` tag correctly. #24667 by Thomas Fan.

- Fix `utils.check_array` now supports Pandas Series with `pd.NA` by raising a better error message or returning a compatible `ndarray`. #25080 by Thomas Fan.

- API Change The extra keyword parameters of `utils.extmath.density` are deprecated and will be removed in 1.4. #24523 by Mia Bajic.

## Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.1, including:

2357juan, 3lLobo, Adam J. Stewart, Adam Kania, Adam Li, Aditya Anulekh, Admir Demiraj, adoublet, Adrin Jalali, Ahmedbgh, Aiko, Akshita Prasanth, Ala-Na, Alessandro Miola, Alex, Alexandr, Alexandre Perez-Lebel, Alex Buzenet, Ali H. El-Kassas, aman kumar, Amit Bera, András Simon, Andreas Grivas, Andreas Mueller, Andrew Wang, angela-maennel, Aniket Shirsat, Anthony22-dev, Antony Lee, anupam, Apostolos Tsetoglou, Aravindh R, Artur Hermano, Arturo Amor, as-90, ashah002, Ashwin Mathur, avm19, Azaria Gebremichael, b0rxington, Badr MOUFAD, Bardiya Ak, Bartłomiej Gońda, BdeGraaff, Benjamin Bossan, Benjamin Carter, berkecanrizai, Bernd Fritzke, Bhoomika, Biswaroop Mitra, Brandon TH Chen, Brett Cannon, Bsh, cache-missing, carlo, Carlos Ramos Carreño, ceh, chalulu, Changyao Chen, Charles Zablit, Chiara Marmo, Christian Lorentzen, Christian Ritter, Christian Veenhuis, christianwaldmann, Christine P. Chai, Claudio Salvatore Arcidiacono, Clément Verrier, crispinlogan, Da-Lan, DanGonite57, Daniela Fernandes, DanielGaerber, darioka, Darren Nguyen, davidblnc, david-cortes, David Gilbertson, David Poznik, Dayne, Dea María Léon, Denis, Dev Khant, Dhanshree Arora, Diadochokinetic, diederikwp, Dimitri Papadopoulos Orfanos, Dimitris Litsidis, drewhogg, Duarte OC, Dwight Lindquist, Eden Brekke, Edern, Edoardo Abati, Eleanore Denies, EliaSchiavon, Emir, ErmolaevPA, Fabrizio Damicelli, fcharras, Felipe Siola, Flynn, francesco-tuveri, Franck Charras, ftorres16, Gael Varoquaux, Geevarghese George, genvalen, GeorgiaMayDay, Gianr Lazz, Gleb Levitski, Glòria Macià Muñoz, Guillaume Lemaitre, Guillem García Subies, Guitared, gunesbayir, Haesun Park, Hansin Ahuja, Hao Chun Chang, Harsh Agrawal, harshit5674, hasan-yaman, henrymooresc, Henry Sorsky, Hristo Vrigazov, htsedebenham, humahn, i-aki-y, Ian Thompson, Ido M, Iglesys, Iliya Zhechev, Irene, ivanllt, Ivan Sedykh, Jack McIvor, jakirkham, JanFidor, Jason G, Jérémie du Boisberranger, Jiten Sidhpura, jkarolczak, João David, JohnathanPi, John Koumentis, John P, John Pangas, johnthagen, Jordan Fleming, Joshua Choo Yun Keat, Jovan Stojanovic, Juan Carlos Alfaro Jiménez, juanfe88, Juan Felipe Arias, JuliaSchoepp, Julien Jerphanion, jygerardy, ka00ri, Kanishk Sachdev, Kanissh, Kaushik Amar Das, Kendall, Kenneth Prabakaran, Kento Nozawa, kernc, Kevin Roice, Kian Eliasi, Kilian Kluge, Kilian Lieret, Kirandevraj, Kraig, krishna kumar, krishna vamsi, Kshitij Kapadni, Kshitij Mathur, Lauren Burke, Léonard Binet, lingyi1110, Lisa Casino, Logan Thomas, Loic Esteve, Luciano Mantovani, Lucy Liu, Maascha, Madhura Jayaratne, madinak, Maksym, Malte S. Kurz, Mansi Agrawal, Marco Edward Gorelli, Marco Wurps, Maren Westermann, Maria Telenczuk, Mario Kostelac, martin-kokos, Marvin Krawutschke, Masanori Kanazu, mathurinm, Matt Haberland, mauroantonioserrano, Max Halford, Maxi Marufo, maximeSaur, Maxim Smolskiy, Maxwell, m. bou, Meekail Zain, Mehgarg, mehmetcanakbay, Mia Bajić, Michael Flaks, Michael Hornstein, Michel de Ruiter, Michelle Paradis, Mikhail Iljin, Misa Ogura, Moritz Wilksch, mrastgoo, Naipawat Poolsawat, Naoise Holohan, Nass, Nathan Jacobi, Nawazish Alam, Nguyễn Văn Diễn, Nicola Fanelli, Nihal Thukarama Rao, Nikita Jare, nima10khodaveisi, Nima Sarajpoor, nitinramvelraj, NNLNR, npache, Nwanna-Joseph, Nymark Kho, o-holman, Olivier Grisel, Olle Lukowski, Omar Hassoun, Omar Salman, osman tamer, ouss1508, Oyindamola Olatunji, PAB, Pandata, partev, Paulo Sergio Soares, Petar Mlinarić, Peter Jansson, Peter Steinbach, Philipp Jung, Piet Brömmel, Pooja M, Pooja Subramaniam, priyam kakati, puhuk, Rachel Freeland, Rachit Keerti Das, Rafal Wojdyla, Raghuveer Bhat, Rahil Parikh, Ralf Gommers, ram vikram singh, Ravi Makhija, Rehan Guha, Reshama Shaikh, Richard Klima, Rob Crockett, Robert Hommes, Robert Juergens, Robin Lenz, Rocco Meli, Roman4oo, Ross Barnowski, Rowan Mankoo, Rudresh Veerkhare, Rushil Desai, Sabri Monaf Sabri, Safikh, Safiuddin Khaja, Salahuddin, Sam Adam Day, Sandra Yojana Meneses, Sandro Ephrem, Sangam, SangamSwadik, SANJAI_3, SarahRemus, Sashka Warner, SavkoMax, Scott Gigante, Scott Gustafson, Sean Atukorala, sec65, SELEE, seljaks, Shady el Gewily, Shane, shellyfung, Shinsuke Mori, Shiva chauhan, Shoaib Khan, Shogo Hida, Shrankhla Srivastava, Shuangchi He, Simon, sonnivs, Sortofamudkip, Srinath Kailasa, Stanislav (Stanley) Modrak, Stefanie Molin, stellalin7, Stéphane Collot, Steven Van Vaerenbergh, Steve Schmerler, Sven Stehle, Tabea Kossen, TheDevPanda, the-syd-sre, Thijs van Weezel, Thomas Bonald, Thomas Germer, Thomas J. Fan, Ti-Ion, Tim Head, Timofei Kornev, toastedyeast, Tobias Pitters, Tom Dupré la Tour, tomiock, Tom Mathews, Tom McTiernan, tspeng, Tyler Egashira, Valentin Laurent, Varun Jain, Vera Komeyer, Vicente Reyes-Puerta, Vinayak Mehta, Vincent M, Vishal, Vyom Pathak, wattai, wchathura, WEN Hao, William M, x110, Xiao Yuan, Xunius, yanhong-zhao-ef, Yusuf Raji, Z Adil Khwaja, zeeshan lone