py-scikit-learn: updated to 1.3.1

Version 1.3.1
=============

Changed models
--------------

The following estimators and functions, when fit with the same data and
parameters, may produce different models from the previous version. This often
occurs due to changes in the modelling logic (bug fixes or enhancements), or in
random sampling procedures.

- |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

Changes impacting all modules
-----------------------------

- |Fix| The `set_output` API correctly works with list input.

Changelog
---------

:mod:`sklearn.calibration`
..........................

- |Fix| :class:`calibration.CalibratedClassifierCV` can now handle models that
  produce large prediction scores. Before it was numerically unstable.

:mod:`sklearn.cluster`
......................

- |Fix| :class:`cluster.BisectingKMeans` could crash when predicting on data
  with a different scale than the data used to fit the model.

- |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature.

:mod:`sklearn.cross_decomposition`
..................................

- |Fix| :class:`cross_decomposition.PLSRegression` now automatically ravels the output
  of `predict` if fitted with one dimensional `y`.

:mod:`sklearn.ensemble`
.......................

- |Fix| Fix a bug in :class:`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).

:mod:`sklearn.feature_selection`
................................

- |Fix| :func:`feature_selection.mutual_info_regression` now correctly computes the
  result when `X` is of integer dtype.

:mod:`sklearn.impute`
.....................

- |Fix| :class:`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``.

:mod:`sklearn.metrics`
......................

- |Fix| Scorers used with :func:`metrics.get_scorer` handle properly
  multilabel-indicator matrix.

:mod:`sklearn.mixture`
......................

- |Fix| The initialization of :class:`mixture.GaussianMixture` from user-provided
  `precisions_init` for `covariance_type` of `full` or `tied` was not correct,
  and has been fixed.

:mod:`sklearn.neighbors`
........................

- |Fix| :meth:`neighbors.KNeighborsClassifier.predict` no longer raises an
  exception for `pandas.DataFrames` input.

- |Fix| Reintroduce :attr:`sklearn.neighbors.BallTree.valid_metrics` and
  :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes.

- |Fix| :class:`sklearn.model_selection.HalvingRandomSearchCV` no longer raises
  when the input to the `param_distributions` parameter is a list of dicts.

- |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`.

:mod:`sklearn.preprocessing`
............................

- |Fix| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword
  argument.

- |Fix| :class:`preprocessing.OneHotEncoder` shows a more informative error message
  when `sparse_output=True` and the output is configured to be pandas.

:mod:`sklearn.tree`
...................

- |Fix| :func:`tree.plot_tree` now accepts `class_names=True` as documented.

- |Fix| The `feature_names` parameter of :func:`tree.plot_tree` now accepts any kind of
  array-like instead of just a list.


(adam)