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# Version 1.3.2

October 2023

## Changelog

### `sklearn.datasets`

- Fix All dataset fetchers now accept `data_home` as any object that implements the `os.PathLike` interface, for instance, `pathlib.Path`. #27468 by Yao Xiao.

### `sklearn.decomposition`

- Fix Fixes a bug in `decomposition.KernelPCA` by forcing the output of the internal `preprocessing.KernelCenterer` to be a default array. When the arpack solver is used, it expects an array with a `dtype` attribute. #27583 by Guillaume Lemaitre.

### `sklearn.metrics`

- Fix Fixes a bug for metrics using `zero_division=np.nan` (e.g. `precision_score`) within a paralell loop (e.g. `cross_val_score`) where the singleton for `np.nan` will be different in the sub-processes. #27573 by Guillaume Lemaitre.

### `sklearn.tree`

- Fix Do not leak data via non-initialized memory in decision tree pickle files and make the generation of those files deterministic. #27580 by Loïc Estève.

# Version 1.3.1

September 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 Ridge models with `solver='sparse_cg'` may have slightly different results with scipy>=1.12, because of an underlying change in the scipy solver (see scipy#18488 for more details) #26814 by Loïc Estève

## Changes impacting all modules

- Fix The `set_output` API correctly works with list input. #27044 by Thomas Fan.

## Changelog

### `sklearn.calibration`

- Fix `calibration.CalibratedClassifierCV` can now handle models that produce large prediction scores. Before it was numerically unstable. #26913 by Omar Salman.

### `sklearn.cluster`

- Fix `cluster.BisectingKMeans` could crash when predicting on data with a different scale than the data used to fit the model. #27167 by Olivier Grisel.

- Fix `cluster.BisectingKMeans` now works with data that has a single feature. #27243 by Jérémie du Boisberranger.

### `sklearn.cross_decomposition`

- Fix `cross_decomposition.PLSRegression` now automatically ravels the output of `predict` if fitted with one dimensional `y`. #26602 by Yao Xiao.

### `sklearn.ensemble`

- Fix Fix a bug in `ensemble.AdaBoostClassifier` with `algorithm="SAMME"` where the decision function of each weak learner should be symmetric (i.e. the sum of the scores should sum to zero for a sample). #26521 by Guillaume Lemaitre.

### `sklearn.feature_selection`

- Fix `feature_selection.mutual_info_regression` now correctly computes the result when `X` is of integer dtype. #26748 by Yao Xiao.

### `sklearn.impute`

- Fix `impute.KNNImputer` now correctly adds a missing indicator column in `transform` when `add_indicator` is set to `True` and missing values are observed during `fit`. #26600 by Shreesha Kumar Bhat.

### `sklearn.metrics`

- Fix Scorers used with `metrics.get_scorer` handle properly multilabel-indicator matrix. #27002 by Guillaume Lemaitre.

### `sklearn.mixture`

- Fix The initialization of `mixture.GaussianMixture` from user-provided `precisions_init` for `covariance_type` of `full` or `tied` was not correct, and has been fixed. #26416 by Yang Tao.

### `sklearn.neighbors`

- Fix `neighbors.KNeighborsClassifier.predict` no longer raises an exception for `pandas.DataFrames` input. #26772 by Jérémie du Boisberranger.

- Fix Reintroduce `sklearn.neighbors.BallTree.valid_metrics` and `sklearn.neighbors.KDTree.valid_metrics` as public class attributes. #26754 by Julien Jerphanion.

- Fix `sklearn.model_selection.HalvingRandomSearchCV` no longer raises when the input to the `param_distributions` parameter is a list of dicts. #26893 by Stefanie Senger.

- Fix Neighbors based estimators now correctly work when `metric="minkowski"` and the metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`. #26760 by Shreesha Kumar Bhat.

### `sklearn.preprocessing`

- Fix `preprocessing.LabelEncoder` correctly accepts `y` as a keyword argument. #26940 by Thomas Fan.

- Fix `preprocessing.OneHotEncoder` shows a more informative error message when `sparse_output=True` and the output is configured to be pandas. #26931 by Thomas Fan.

### `sklearn.tree`

- Fix `tree.plot_tree` now accepts `class_names=True` as documented. #26903 by Thomas Roehr

- Fix The `feature_names` parameter of `tree.plot_tree` now accepts any kind of array-like instead of just a list. #27292 by Rahil Parikh.

# Version 1.3.0

June 2023

For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.3.

## 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 `multiclass.OutputCodeClassifier.predict` now uses a more efficient pairwise distance reduction. As a consequence, the tie-breaking strategy is different and thus the predicted labels may be different. #25196 by Guillaume Lemaitre.

- Enhancement The `fit_transform` method of `decomposition.DictionaryLearning` is more efficient but may produce different results as in previous versions when `transform_algorithm` is not the same as `fit_algorithm` and the number of iterations is small. #24871 by Omar Salman.

- Enhancement The `sample_weight` parameter now will be used in centroids initialization for `cluster.KMeans`, `cluster.BisectingKMeans` and `cluster.MiniBatchKMeans`. This change will break backward compatibility, since numbers generated from same random seeds will be different. #25752 by Gleb Levitski, Jérémie du Boisberranger, Guillaume Lemaitre.

- Fix Treat more consistently small values in the `W` and `H` matrices during the `fit` and `transform` steps of `decomposition.NMF` and `decomposition.MiniBatchNMF` which can produce different results than previous versions. #25438 by Yotam Avidar-Constantini.

- Fix `decomposition.KernelPCA` may produce different results through `inverse_transform` if `gamma` is `None`. Now it will be chosen correctly as `1/n_features` of the data that it is fitted on, while previously it might be incorrectly chosen as `1/n_features` of the data passed to `inverse_transform`. A new attribute `gamma_` is provided for revealing the actual value of `gamma` used each time the kernel is called. #26337 by Yao Xiao.

## Changed displays

- Enhancement `model_selection.LearningCurveDisplay` displays both the train and test curves by default. You can set `score_type="test"` to keep the past behaviour. #25120 by Guillaume Lemaitre.

- Fix `model_selection.ValidationCurveDisplay` now accepts passing a list to the `param_range` parameter. #27311 by Arturo Amor.

## Changes impacting all modules

- Enhancement The `get_feature_names_out` method of the following classes now raises a `NotFittedError` if the instance is not fitted. This ensures the error is consistent in all estimators with the `get_feature_names_out` method.

  - `impute.MissingIndicator`

  - `feature_extraction.DictVectorizer`

  - `feature_extraction.text.TfidfTransformer`

  - `feature_selection.GenericUnivariateSelect`

  - `feature_selection.RFE`

  - `feature_selection.RFECV`

  - `feature_selection.SelectFdr`

  - `feature_selection.SelectFpr`

  - `feature_selection.SelectFromModel`

  - `feature_selection.SelectFwe`

  - `feature_selection.SelectKBest`

  - `feature_selection.SelectPercentile`

  - `feature_selection.SequentialFeatureSelector`

  - `feature_selection.VarianceThreshold`

  - `kernel_approximation.AdditiveChi2Sampler`

  - `impute.IterativeImputer`

  - `impute.KNNImputer`

  - `impute.SimpleImputer`

  - `isotonic.IsotonicRegression`

  - `preprocessing.Binarizer`

  - `preprocessing.KBinsDiscretizer`

  - `preprocessing.MaxAbsScaler`

  - `preprocessing.MinMaxScaler`

  - `preprocessing.Normalizer`

  - `preprocessing.OrdinalEncoder`

  - `preprocessing.PowerTransformer`

  - `preprocessing.QuantileTransformer`

  - `preprocessing.RobustScaler`

  - `preprocessing.SplineTransformer`

  - `preprocessing.StandardScaler`

  - `random_projection.GaussianRandomProjection`

  - `random_projection.SparseRandomProjection`

  The `NotFittedError` displays an informative message asking to fit the instance with the appropriate arguments.

  #25294, #25308, #25291, #25367, #25402, by John Pangas, Rahil Parikh , and Alex Buzenet.

- Enhancement Added a multi-threaded Cython routine to the compute squared Euclidean distances (sometimes followed by a fused reduction operation) for a pair of datasets consisting of a sparse CSR matrix and a dense NumPy.

  This can improve the performance of following functions and estimators:

  - `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`

  A typical example of this performance improvement happens when passing a sparse CSR matrix to the `predict` or `transform` method of estimators that rely on a dense NumPy representation to store their fitted parameters (or the reverse).

  For instance, `sklearn.NearestNeighbors.kneighbors` is now up to 2 times faster for this case on commonly available laptops.

  #25044 by Julien Jerphanion.

- Enhancement All estimators that internally rely on OpenMP multi-threading (via Cython) now use a number of threads equal to the number of physical (instead of logical) cores by default. In the past, we observed that using as many threads as logical cores on SMT hosts could sometimes cause severe performance problems depending on the algorithms and the shape of the data. Note that it is still possible to manually adjust the number of threads used by OpenMP as documented in Parallelism.

  #26082 by Jérémie du Boisberranger and Olivier Grisel.

## Experimental / Under Development

- Major Feature Metadata routing’s related base methods are included in this release. This feature is only available via the `enable_metadata_routing` feature flag which can be enabled using `sklearn.set_config` and `sklearn.config_context`. For now this feature is mostly useful for third party developers to prepare their code base for metadata routing, and we strongly recommend that they also hide it behind the same feature flag, rather than having it enabled by default. #24027 by Adrin Jalali, Benjamin Bossan, and Omar Salman.

## Changelog

### `sklearn`

- Feature Added a new option `skip_parameter_validation`, to the function `sklearn.set_config` and context manager `sklearn.config_context`, that allows to skip the validation of the parameters passed to the estimators and public functions. This can be useful to speed up the code but should be used with care because it can lead to unexpected behaviors or raise obscure error messages when setting invalid parameters. #25815 by Jérémie du Boisberranger.

### `sklearn.base`

- Feature A `__sklearn_clone__` protocol is now available to override the default behavior of `base.clone`. #24568 by Thomas Fan.

- Fix `base.TransformerMixin` now currently keeps a namedtuple’s class if `transform` returns a namedtuple. #26121 by Thomas Fan.

### `sklearn.calibration`

- Fix `calibration.CalibratedClassifierCV` now does not enforce sample alignment on `fit_params`. #25805 by Adrin Jalali.

### `sklearn.cluster`

- Major Feature Added `cluster.HDBSCAN`, a modern hierarchical density-based clustering algorithm. Similarly to `cluster.OPTICS`, it can be seen as a generalization of `cluster.DBSCAN` by allowing for hierarchical instead of flat clustering, however it varies in its approach from `cluster.OPTICS`. This algorithm is very robust with respect to its hyperparameters’ values and can be used on a wide variety of data without much, if any, tuning.

  This implementation is an adaptation from the original implementation of HDBSCAN in scikit-learn-contrib/hdbscan, by Leland McInnes et al.

  #26385 by Meekail Zain

- Enhancement The `sample_weight` parameter now will be used in centroids initialization for `cluster.KMeans`, `cluster.BisectingKMeans` and `cluster.MiniBatchKMeans`. This change will break backward compatibility, since numbers generated from same random seeds will be different. #25752 by Gleb Levitski, Jérémie du Boisberranger, Guillaume Lemaitre.

- Fix `cluster.KMeans`, `cluster.MiniBatchKMeans` and `cluster.k_means` now correctly handle the combination of `n_init="auto"` and `init` being an array-like, running one initialization in that case. #26657 by Binesh Bannerjee.

- API Change The `sample_weight` parameter in `predict` for `cluster.KMeans.predict` and `cluster.MiniBatchKMeans.predict` is now deprecated and will be removed in v1.5. #25251 by Gleb Levitski.

- API Change The `Xred` argument in `cluster.FeatureAgglomeration.inverse_transform` is renamed to `Xt` and will be removed in v1.5. #26503 by Adrin Jalali.

### `sklearn.compose`

- Fix `compose.ColumnTransformer` raises an informative error when the individual transformers of `ColumnTransformer` output pandas dataframes with indexes that are not consistent with each other and the output is configured to be pandas. #26286 by Thomas Fan.

- Fix `compose.ColumnTransformer` correctly sets the output of the remainder when `set_output` is called. #26323 by Thomas Fan.

### `sklearn.covariance`

- Fix Allows `alpha=0` in `covariance.GraphicalLasso` to be consistent with `covariance.graphical_lasso`. #26033 by Genesis Valencia.

- Fix `covariance.empirical_covariance` now gives an informative error message when input is not appropriate. #26108 by Quentin Barthélemy.

- API Change Deprecates `cov_init` in `covariance.graphical_lasso` in 1.3 since the parameter has no effect. It will be removed in 1.5. #26033 by Genesis Valencia.

- API Change Adds `costs_` fitted attribute in `covariance.GraphicalLasso` and `covariance.GraphicalLassoCV`. #26033 by Genesis Valencia.

- API Change Adds `covariance` parameter in `covariance.GraphicalLasso`. #26033 by Genesis Valencia.

- API Change Adds `eps` parameter in `covariance.GraphicalLasso`, `covariance.graphical_lasso`, and `covariance.GraphicalLassoCV`. #26033 by Genesis Valencia.

### `sklearn.datasets`

- Enhancement Allows to overwrite the parameters used to open the ARFF file using the parameter `read_csv_kwargs` in `datasets.fetch_openml` when using the pandas parser. #26433 by Guillaume Lemaitre.

- Fix `datasets.fetch_openml` returns improved data types when `as_frame=True` and `parser="liac-arff"`. #26386 by Thomas Fan.

- Fix Following the ARFF specs, only the marker `"?"` is now considered as a missing values when opening ARFF files fetched using `datasets.fetch_openml` when using the pandas parser. The parameter `read_csv_kwargs` allows to overwrite this behaviour. #26551 by Guillaume Lemaitre.

- Fix `datasets.fetch_openml` will consistenly use `np.nan` as missing marker with both parsers `"pandas"` and `"liac-arff"`. #26579 by Guillaume Lemaitre.

- API Change The `data_transposed` argument of `datasets.make_sparse_coded_signal` is deprecated and will be removed in v1.5. #25784 by @Jérémie du Boisberranger.

### `sklearn.decomposition`

- Efficiency `decomposition.MiniBatchDictionaryLearning` and `decomposition.MiniBatchSparsePCA` are now faster for small batch sizes by avoiding duplicate validations. #25490 by Jérémie du Boisberranger.

- Enhancement `decomposition.DictionaryLearning` now accepts the parameter `callback` for consistency with the function `decomposition.dict_learning`. #24871 by Omar Salman.

- Fix Treat more consistently small values in the `W` and `H` matrices during the `fit` and `transform` steps of `decomposition.NMF` and `decomposition.MiniBatchNMF` which can produce different results than previous versions. #25438 by Yotam Avidar-Constantini.

- API Change The `W` argument in `decomposition.NMF.inverse_transform` and `decomposition.MiniBatchNMF.inverse_transform` is renamed to `Xt` and will be removed in v1.5. #26503 by Adrin Jalali.

### `sklearn.discriminant_analysis`

- Enhancement `discriminant_analysis.LinearDiscriminantAnalysis` now supports the PyTorch. See Array API support (experimental) for more details. #25956 by Thomas Fan.

### `sklearn.ensemble`

- Feature `ensemble.HistGradientBoostingRegressor` now supports the Gamma deviance loss via `loss="gamma"`. Using the Gamma deviance as loss function comes in handy for modelling skewed distributed, strictly positive valued targets. #22409 by Christian Lorentzen.

- Feature Compute a custom out-of-bag score by passing a callable to `ensemble.RandomForestClassifier`, `ensemble.RandomForestRegressor`, `ensemble.ExtraTreesClassifier` and `ensemble.ExtraTreesRegressor`. #25177 by Tim Head.

- Feature `ensemble.GradientBoostingClassifier` now exposes out-of-bag scores via the `oob_scores_` or `oob_score_` attributes. #24882 by Ashwin Mathur.

- Efficiency `ensemble.IsolationForest` predict time is now faster (typically by a factor of 8 or more). Internally, the estimator now precomputes decision path lengths per tree at `fit` time. It is therefore not possible to load an estimator trained with scikit-learn 1.2 to make it predict with scikit-learn 1.3: retraining with scikit-learn 1.3 is required. #25186 by Felipe Breve Siola.

- Efficiency `ensemble.RandomForestClassifier` and `ensemble.RandomForestRegressor` with `warm_start=True` now only recomputes out-of-bag scores when there are actually more `n_estimators` in subsequent `fit` calls. #26318 by Joshua Choo Yun Keat.

- Enhancement `ensemble.BaggingClassifier` and `ensemble.BaggingRegressor` expose the `allow_nan` tag from the underlying estimator. #25506 by Thomas Fan.

- Fix `ensemble.RandomForestClassifier.fit` sets `max_samples = 1` when `max_samples` is a float and `round(n_samples * max_samples) < 1`. #25601 by Jan Fidor.

- Fix `ensemble.IsolationForest.fit` no longer warns about missing feature names when called with `contamination` not `"auto"` on a pandas dataframe. #25931 by Yao Xiao.

- Fix `ensemble.HistGradientBoostingRegressor` and `ensemble.HistGradientBoostingClassifier` treats negative values for categorical features consistently as missing values, following LightGBM’s and pandas’ conventions. #25629 by Thomas Fan.

- Fix Fix deprecation of `base_estimator` in `ensemble.AdaBoostClassifier` and `ensemble.AdaBoostRegressor` that was introduced in #23819. #26242 by Marko Toplak.

### `sklearn.exceptions`

- Feature Added `exceptions.InconsistentVersionWarning` which is raised when a scikit-learn estimator is unpickled with a scikit-learn version that is inconsistent with the sckit-learn version the estimator was pickled with. #25297 by Thomas Fan.

### `sklearn.feature_extraction`

- API Change `feature_extraction.image.PatchExtractor` now follows the transformer API of scikit-learn. This class is defined as a stateless transformer meaning that it is note required to call `fit` before calling `transform`. Parameter validation only happens at `fit` time. #24230 by Guillaume Lemaitre.

### `sklearn.feature_selection`

- Enhancement All selectors in `sklearn.feature_selection` will preserve a DataFrame’s dtype when transformed. #25102 by Thomas Fan.

- Fix `feature_selection.SequentialFeatureSelector`’s `cv` parameter now supports generators. #25973 by `Yao Xiao <Charlie-XIAO>`.

### `sklearn.impute`

- Enhancement Added the parameter `fill_value` to `impute.IterativeImputer`. #25232 by Thijs van Weezel.

- Fix `impute.IterativeImputer` now correctly preserves the Pandas Index when the `set_config(transform_output="pandas")`. #26454 by Thomas Fan.

### `sklearn.inspection`

- Enhancement Added support for `sample_weight` in `inspection.partial_dependence` and `inspection.PartialDependenceDisplay.from_estimator`. This allows for weighted averaging when aggregating for each value of the grid we are making the inspection on. The option is only available when `method` is set to `brute`. #25209 and #26644 by Carlo Lemos.

- API Change `inspection.partial_dependence` returns a `utils.Bunch` with new key: `grid_values`. The `values` key is deprecated in favor of `grid_values` and the `values` key will be removed in 1.5. #21809 and #25732 by Thomas Fan.

### `sklearn.kernel_approximation`

- Fix `kernel_approximation.AdditiveChi2Sampler` is now stateless. The `sample_interval_` attribute is deprecated and will be removed in 1.5. #25190 by Vincent Maladière.

### `sklearn.linear_model`

- Efficiency Avoid data scaling when `sample_weight=None` and other unnecessary data copies and unexpected dense to sparse data conversion in `linear_model.LinearRegression`. #26207 by Olivier Grisel.

- Enhancement `linear_model.SGDClassifier`, `linear_model.SGDRegressor` and `linear_model.SGDOneClassSVM` now preserve dtype for `numpy.float32`. #25587 by Omar Salman.

- Enhancement The `n_iter_` attribute has been included in `linear_model.ARDRegression` to expose the actual number of iterations required to reach the stopping criterion. #25697 by John Pangas.

- Fix Use a more robust criterion to detect convergence of `linear_model.LogisticRegression` with `penalty="l1"` and `solver="liblinear"` on linearly separable problems. #25214 by Tom Dupre la Tour.

- Fix Fix a crash when calling `fit` on `linear_model.LogisticRegression` with `solver="newton-cholesky"` and `max_iter=0` which failed to inspect the state of the model prior to the first parameter update. #26653 by Olivier Grisel.

- API Change Deprecates `n_iter` in favor of `max_iter` in `linear_model.BayesianRidge` and `linear_model.ARDRegression`. `n_iter` will be removed in scikit-learn 1.5. This change makes those estimators consistent with the rest of estimators. #25697 by John Pangas.

### `sklearn.manifold`

- Fix `manifold.Isomap` now correctly preserves the Pandas Index when the `set_config(transform_output="pandas")`. #26454 by Thomas Fan.

### `sklearn.metrics`

- Feature Adds `zero_division=np.nan` to multiple classification metrics: `metrics.precision_score`, `metrics.recall_score`, `metrics.f1_score`, `metrics.fbeta_score`, `metrics.precision_recall_fscore_support`, `metrics.classification_report`. When `zero_division=np.nan` and there is a zero division, the metric is undefined and is excluded from averaging. When not used for averages, the value returned is `np.nan`. #25531 by Marc Torrellas Socastro.

- Feature `metrics.average_precision_score` now supports the multiclass case. #17388 by Geoffrey Bolmier and #24769 by Ashwin Mathur.

- Efficiency The computation of the expected mutual information in `metrics.adjusted_mutual_info_score` is now faster when the number of unique labels is large and its memory usage is reduced in general. #25713 by Kshitij Mathur, Guillaume Lemaitre, Omar Salman and Jérémie du Boisberranger.

- Enhancement `metrics.silhouette_samples` nows accepts a sparse matrix of pairwise distances between samples, or a feature array. #18723 by Sahil Gupta and #24677 by Ashwin Mathur.

- Enhancement A new parameter `drop_intermediate` was added to `metrics.precision_recall_curve`, `metrics.PrecisionRecallDisplay.from_estimator`, `metrics.PrecisionRecallDisplay.from_predictions`, which drops some suboptimal thresholds to create lighter precision-recall curves. #24668 by @dberenbaum.

- Enhancement `metrics.RocCurveDisplay.from_estimator` and `metrics.RocCurveDisplay.from_predictions` now accept two new keywords, `plot_chance_level` and `chance_level_kw` to plot the baseline chance level. This line is exposed in the `chance_level_` attribute. #25987 by Yao Xiao.

- Enhancement `metrics.PrecisionRecallDisplay.from_estimator` and `metrics.PrecisionRecallDisplay.from_predictions` now accept two new keywords, `plot_chance_level` and `chance_level_kw` to plot the baseline chance level. This line is exposed in the `chance_level_` attribute. #26019 by Yao Xiao.

- Fix `metrics.pairwise.manhattan_distances` now supports readonly sparse datasets. #25432 by Julien Jerphanion.

- Fix Fixed `metrics.classification_report` so that empty input will return `np.nan`. Previously, “macro avg” and `weighted avg` would return e.g. `f1-score=np.nan` and `f1-score=0.0`, being inconsistent. Now, they both return `np.nan`. #25531 by Marc Torrellas Socastro.

- Fix `metrics.ndcg_score` now gives a meaningful error message for input of length 1. #25672 by Lene Preuss and Wei-Chun Chu.

- Fix `metrics.log_loss` raises a warning if the values of the parameter `y_pred` are not normalized, instead of actually normalizing them in the metric. Starting from 1.5 this will raise an error. #25299 by @Omar Salman <OmarManzoor.

- Fix In `metrics.roc_curve`, use the threshold value `np.inf` instead of arbitrary `max(y_score) + 1`. This threshold is associated with the ROC curve point `tpr=0` and `fpr=0`. #26194 by Guillaume Lemaitre.

- Fix The `'matching'` metric has been removed when using SciPy>=1.9 to be consistent with `scipy.spatial.distance` which does not support `'matching'` anymore. #26264 by Barata T. Onggo

- API Change The `eps` parameter of the `metrics.log_loss` has been deprecated and will be removed in 1.5. #25299 by Omar Salman.

### `sklearn.gaussian_process`

- Fix `gaussian_process.GaussianProcessRegressor` has a new argument `n_targets`, which is used to decide the number of outputs when sampling from the prior distributions. #23099 by Zhehao Liu.

### `sklearn.model_selection`

- Major Feature Added the class `model_selection.ValidationCurveDisplay` that allows easy plotting of validation curves obtained by the function `model_selection.validation_curve`. #25120 by Guillaume Lemaitre.

- API Change The parameter `log_scale` in the class `model_selection.LearningCurveDisplay` has been deprecated in 1.3 and will be removed in 1.5. The default scale can be overriden by setting it directly on the `ax` object and will be set automatically from the spacing of the data points otherwise. #25120 by Guillaume Lemaitre.

- Enhancement `model_selection.cross_validate` accepts a new parameter `return_indices` to return the train-test indices of each cv split. #25659 by Guillaume Lemaitre.

### `sklearn.multioutput`

- Fix `getattr` on `multioutput.MultiOutputRegressor.partial_fit` and `multioutput.MultiOutputClassifier.partial_fit` now correctly raise an `AttributeError` if done before calling `fit`. #26333 by Adrin Jalali.

### `sklearn.naive_bayes`

- Fix `naive_bayes.GaussianNB` does not raise anymore a `ZeroDivisionError` when the provided `sample_weight` reduces the problem to a single class in `fit`. #24140 by Jonathan Ohayon and Chiara Marmo.

### `sklearn.neighbors`

- Enhancement The performance of `neighbors.KNeighborsClassifier.predict` and of `neighbors.KNeighborsClassifier.predict_proba` has been improved when `n_neighbors` is large and `algorithm="brute"` with non Euclidean metrics. #24076 by Meekail Zain, Julien Jerphanion.

- Fix Remove support for `KulsinskiDistance` in `neighbors.BallTree`. This dissimilarity is not a metric and cannot be supported by the BallTree. #25417 by Guillaume Lemaitre.

- API Change The support for metrics other than `euclidean` and `manhattan` and for callables in `neighbors.NearestNeighbors` is deprecated and will be removed in version 1.5. #24083 by Valentin Laurent.

### `sklearn.neural_network`

- Fix `neural_network.MLPRegressor` and `neural_network.MLPClassifier` reports the right `n_iter_` when `warm_start=True`. It corresponds to the number of iterations performed on the current call to `fit` instead of the total number of iterations performed since the initialization of the estimator. #25443 by Marvin Krawutschke.

### `sklearn.pipeline`

- Feature `pipeline.FeatureUnion` can now use indexing notation (e.g. `feature_union["scalar"]`) to access transformers by name. #25093 by Thomas Fan.

- Feature `pipeline.FeatureUnion` can now access the `feature_names_in_` attribute if the `X` value seen during `.fit` has a `columns` attribute and all columns are strings. e.g. when `X` is a `pandas.DataFrame` #25220 by Ian Thompson.

- Fix `pipeline.Pipeline.fit_transform` now raises an `AttributeError` if the last step of the pipeline does not support `fit_transform`. #26325 by Adrin Jalali.

### `sklearn.preprocessing`

- Major Feature Introduces `preprocessing.TargetEncoder` which is a categorical encoding based on target mean conditioned on the value of the category. #25334 by Thomas Fan.

- Feature `preprocessing.OrdinalEncoder` 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`. #25677 by Thomas Fan.

- Enhancement `preprocessing.PolynomialFeatures` now calculates the number of expanded terms a-priori when dealing with sparse `csr` matrices in order to optimize the choice of `dtype` for `indices` and `indptr`. It can now output `csr` matrices with `np.int32` `indices/indptr` components when there are few enough elements, and will automatically use `np.int64` for sufficiently large matrices. #20524 by niuk-a and #23731 by Meekail Zain

- Enhancement A new parameter `sparse_output` was added to `preprocessing.SplineTransformer`, available as of SciPy 1.8. If `sparse_output=True`, `preprocessing.SplineTransformer` returns a sparse CSR matrix. #24145 by Christian Lorentzen.

- Enhancement Adds a `feature_name_combiner` parameter to `preprocessing.OneHotEncoder`. This specifies a custom callable to create feature names to be returned by `preprocessing.OneHotEncoder.get_feature_names_out`. The callable combines input arguments `(input_feature, category)` to a string. #22506 by Mario Kostelac.

- Enhancement Added support for `sample_weight` in `preprocessing.KBinsDiscretizer`. This allows specifying the parameter `sample_weight` for each sample to be used while fitting. The option is only available when `strategy` is set to `quantile` and `kmeans`. #24935 by Seladus, Guillaume Lemaitre, and Dea María Léon, #25257 by Gleb Levitski.

- Enhancement Subsampling through the `subsample` parameter can now be used in `preprocessing.KBinsDiscretizer` regardless of the strategy used. #26424 by Jérémie du Boisberranger.

- Fix `preprocessing.PowerTransformer` now correctly preserves the Pandas Index when the `set_config(transform_output="pandas")`. #26454 by Thomas Fan.

- Fix `preprocessing.PowerTransformer` now correcly raises error when using `method="box-cox"` on data with a constant `np.nan` column. #26400 by Yao Xiao.

- Fix `preprocessing.PowerTransformer` with `method="yeo-johnson"` now leaves constant features unchanged instead of transforming with an arbitrary value for the `lambdas_` fitted parameter. #26566 by Jérémie du Boisberranger.

- API Change The default value of the `subsample` parameter of `preprocessing.KBinsDiscretizer` will change from `None` to `200_000` in version 1.5 when `strategy="kmeans"` or `strategy="uniform"`. #26424 by Jérémie du Boisberranger.

### `sklearn.svm`

- API Change `dual` parameter now accepts `auto` option for `svm.LinearSVC` and `svm.LinearSVR`. #26093 by Gleb Levitski.

### `sklearn.tree`

- Major Feature `tree.DecisionTreeRegressor` and `tree.DecisionTreeClassifier` support missing values when `splitter='best'` and criterion is `gini`, `entropy`, or `log_loss`, for classification or `squared_error`, `friedman_mse`, or `poisson` for regression. #23595, #26376 by Thomas Fan.

- Enhancement Adds a `class_names` parameter to `tree.export_text`. This allows specifying the parameter `class_names` for each target class in ascending numerical order. #25387 by William M and crispinlogan.

- Fix `tree.export_graphviz` and `tree.export_text` now accepts `feature_names` and `class_names` as array-like rather than lists. #26289 by Yao Xiao

### `sklearn.utils`

- Fix Fixes `utils.check_array` to properly convert pandas extension arrays. #25813 and #26106 by Thomas Fan.

- Fix `utils.check_array` now supports pandas DataFrames with extension arrays and object dtypes by return an ndarray with object dtype. #25814 by Thomas Fan.

- API Change `utils.estimator_checks.check_transformers_unfitted_stateless` has been introduced to ensure stateless transformers don’t raise `NotFittedError` during `transform` with no prior call to `fit` or `fit_transform`. #25190 by Vincent Maladière.

- API Change A `FutureWarning` is now raised when instantiating a class which inherits from a deprecated base class (i.e. decorated by `utils.deprecated`) and which overrides the `__init__` method. #25733 by Brigitta Sipőcz and Jérémie du Boisberranger.

### `sklearn.semi_supervised`

- Enhancement `semi_supervised.LabelSpreading.fit` and `semi_supervised.LabelPropagation.fit` now accepts sparse metrics. #19664 by Kaushik Amar Das.

### Miscellaneous

- Enhancement Replace obsolete exceptions `EnvironmentError`, `IOError` and `WindowsError`. #26466 by Dimitri Papadopoulos ORfanos.

## Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.2, including:

2357juan, Abhishek Singh Kushwah, Adam Handke, Adam Kania, Adam Li, adienes, Admir Demiraj, adoublet, Adrin Jalali, A.H.Mansouri, Ahmedbgh, Ala-Na, Alex Buzenet, AlexL, Ali H. El-Kassas, amay, András Simon, André Pedersen, Andrew Wang, Ankur Singh, annegnx, Ansam Zedan, Anthony22-dev, Artur Hermano, Arturo Amor, as-90, ashah002, Ashish Dutt, Ashwin Mathur, AymericBasset, Azaria Gebremichael, Barata Tripramudya Onggo, Benedek Harsanyi, Benjamin Bossan, Bharat Raghunathan, Binesh Bannerjee, Boris Feld, Brendan Lu, Brevin Kunde, cache-missing, Camille Troillard, Carla J, carlo, Carlo Lemos, c-git, Changyao Chen, Chiara Marmo, Christian Lorentzen, Christian Veenhuis, Christine P. Chai, crispinlogan, Da-Lan, DanGonite57, Dave Berenbaum, davidblnc, david-cortes, Dayne, Dea María Léon, Denis, Dimitri Papadopoulos Orfanos, Dimitris Litsidis, Dmitry Nesterov, Dominic Fox, Dominik Prodinger, Edern, Ekaterina Butyugina, Elabonga Atuo, Emir, farhan khan, Felipe Siola, futurewarning, Gael Varoquaux, genvalen, Gleb Levitski, Guillaume Lemaitre, gunesbayir, Haesun Park, hujiahong726, i-aki-y, Ian Thompson, Ido M, Ily, Irene, Jack McIvor, jakirkham, James Dean, JanFidor, Jarrod Millman, JB Mountford, Jérémie du Boisberranger, Jessicakk0711, Jiawei Zhang, Joey Ortiz, JohnathanPi, John Pangas, Joshua Choo Yun Keat, Joshua Hedlund, JuliaSchoepp, Julien Jerphanion, jygerardy, ka00ri, Kaushik Amar Das, Kento Nozawa, Kian Eliasi, Kilian Kluge, Lene Preuss, Linus, Logan Thomas, Loic Esteve, Louis Fouquet, Lucy Liu, Madhura Jayaratne, Marc Torrellas Socastro, Maren Westermann, Mario Kostelac, Mark Harfouche, Marko Toplak, Marvin Krawutschke, Masanori Kanazu, mathurinm, Matt Haberland, Max Halford, maximeSaur, Maxwell Liu, m. bou, mdarii, Meekail Zain, Mikhail Iljin, murezzda, Nawazish Alam, Nicola Fanelli, Nightwalkx, Nikolay Petrov, Nishu Choudhary, NNLNR, npache, Olivier Grisel, Omar Salman, ouss1508, PAB, Pandata, partev, Peter Piontek, Phil, pnucci, Pooja M, Pooja Subramaniam, precondition, Quentin Barthélemy, Rafal Wojdyla, Raghuveer Bhat, Rahil Parikh, Ralf Gommers, ram vikram singh, Rushil Desai, Sadra Barikbin, SANJAI_3, Sashka Warner, Scott Gigante, Scott Gustafson, searchforpassion, Seoeun Hong, Shady el Gewily, Shiva chauhan, Shogo Hida, Shreesha Kumar Bhat, sonnivs, Sortofamudkip, Stanislav (Stanley) Modrak, Stefanie Senger, Steven Van Vaerenbergh, Tabea Kossen, Théophile Baranger, Thijs van Weezel, Thomas A Caswell, Thomas Germer, Thomas J. Fan, Tim Head, Tim P, Tom Dupré la Tour, tomiock, tspeng, Valentin Laurent, Veghit, VIGNESH D, Vijeth Moudgalya, Vinayak Mehta, Vincent M, Vincent-violet, Vyom Pathak, William M, windiana42, Xiao Yuan, Yao Xiao, Yaroslav Halchenko, Yotam Avidar-Constantini, Yuchen Zhou, Yusuf Raji, zeeshan lone