- update to 1.4.2:
  * This release only includes support for numpy 2.

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- update to 1.3.2:
  * All dataset fetchers now accept `data_home` as any object that
    implements the :class:`os.PathLike` interface, for instance,
    :class:`pathlib.Path`.
  * Fixes a bug in :class:`decomposition.KernelPCA` by forcing the
    output of the internal :class:`preprocessing.KernelCenterer` to
    be a default array. When the arpack solver is used, it expects
    an array with a `dtype` attribute.
  * Fixes a bug for metrics using `zero_division=np.nan`
    (e.g. :func:`~metrics.precision_score`) within a paralell loop
    (e.g. :func:`~model_selection.cross_val_score`) where the
    singleton for `np.nan` will be different in the sub-processes.
  * Do not leak data via non-initialized memory in decision tree
    pickle files and make the generation of those files
    deterministic.
  * Ridge models with `solver='sparse_cg'` may have slightly
    different results with scipy>=1.12, because of an underlying
    change in the scipy solver
  * The `set_output` API correctly works with list input.
  * :class:`calibration.CalibratedClassifierCV` can now handle
    models that produce large prediction scores.

- Skip another recalcitrant test on 32 bit.
  * We are in the process of introducing a new way to route metadata
    such as sample_weight throughout the codebase, which would
    affect how meta-estimators such as pipeline.Pipeline and
  * Originally hosted in the scikit-learn-contrib repository,
  * A new category encoding strategy preprocessing.TargetEncoder
    encodes the categories based on a shrunk estimate of the average
  * The classes tree.DecisionTreeClassifier and tree.DecisionTreeRegressor

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- Skip another recalcitrant test on 32 bit.

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- Update to 1.0.2: 
  * Fixed an infinite loop in cluster.SpectralClustering by moving an iteration counter from try to except. #21271 by Tyler Martin.
  * datasets.fetch_openml is now thread safe. Data is first downloaded to a temporary subfolder and then renamed. #21833 by Siavash Rezazadeh.
  * Fixed the constraint on the objective function of decomposition.DictionaryLearning, decomposition.MiniBatchDictionaryLearning, decomposition.SparsePCA and decomposition.MiniBatchSparsePCA to be convex and match the referenced article. #19210 by Jérémie du Boisberranger.
  * ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, and ensemble.RandomTreesEmbedding now raise a ValueError when bootstrap=False and max_samples is not None. #21295 Haoyin Xu.
  * Solve a bug in ensemble.GradientBoostingClassifier where the exponential loss was computing the positive gradient instead of the negative one. #22050 by Guillaume Lemaitre.
  * Fixed feature_selection.SelectFromModel by improving support for base estimators that do not set feature_names_in_. #21991 by Thomas Fan.
  * Fix a bug in linear_model.RidgeClassifierCV where the method predict was performing an argmax on the scores obtained from decision_function instead of returning the multilabel indicator matrix. #19869 by Guillaume Lemaitre.
  * 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.
  * Fixed an unnecessary error when fitting manifold.Isomap with a precomputed dense distance matrix where the neighbors graph has multiple disconnected components. #21915 by Tom Dupre la Tour.
  * All sklearn.metrics.DistanceMetric subclasses now correctly support read-only buffer attributes. This fixes a regression introduced in 1.0.0 with respect to 0.24.2. #21694 by Julien Jerphanion.
  * neighbors.KDTree and neighbors.BallTree correctly supports read-only buffer attributes. #21845 by Thomas Fan.
  * Fixes compatibility bug with NumPy 1.22 in preprocessing.OneHotEncoder. #21517 by Thomas Fan.
  * Prevents tree.plot_tree from drawing out of the boundary of the figure. #21917 by Thomas Fan.
  * Support loading pickles of decision tree models when the pickle has been generated on a platform with a different bitness. A typical example is to train and pickle the model on 64 bit machine and load the model on a 32 bit machine for prediction. #21552 by Loïc Estève.
  * Non-fit methods in the following classes do not raise a UserWarning when fitted on DataFrames with valid feature names: covariance.EllipticEnvelope, ensemble.IsolationForest, ensemble.AdaBoostClassifier, neighbors.KNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsClassifier, neighbors.RadiusNeighborsRegressor. #21199 by Thomas Fan.
  * Fixed calibration.CalibratedClassifierCV to take into account sample_weight when computing the base estimator prediction when ensemble=False. #20638 by Julien Bohné.
  * Fixed a bug in calibration.CalibratedClassifierCV with method="sigmoid" that was ignoring the sample_weight when computing the the Bayesian priors. #21179 by Guillaume Lemaitre.
  * Compute y_std properly with multi-target in sklearn.gaussian_process.GaussianProcessRegressor allowing proper normalization in multi-target scene. #20761 by Patrick de C. T. R. Ferreira.
  * Fixed a bug in feature_extraction.CountVectorizer and feature_extraction.TfidfVectorizer by raising an error when ‘min_idf’ or ‘max_idf’ are floating-point numbers greater than 1. #20752 by Alek Lefebvre.
  * linear_model.LogisticRegression now raises a better error message when the solver does not support sparse matrices with int64 indices. #21093 by Tom Dupre la Tour.
  * neighbors.KNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsClassifier, neighbors.RadiusNeighborsRegressor with metric="precomputed" raises an error for bsr and dok sparse matrices in methods: fit, kneighbors and radius_neighbors, due to handling of explicit zeros in bsr and dok sparse graph formats. #21199 by Thomas Fan.
  * pipeline.Pipeline.get_feature_names_out correctly passes feature names out from one step of a pipeline to the next. #21351 by Thomas Fan.
  * svm.SVC and svm.SVR check for an inconsistency in its internal representation and raise an error instead of segfaulting. This fix also resolves CVE-2020-28975. #21336 by Thomas Fan.
  * manifold.TSNE now avoids numerical underflow issues during affinity matrix computation.
  * manifold.Isomap now connects disconnected components of the neighbors graph along some minimum distance pairs, instead of changing every infinite distances to zero.
  * Many others, see full changelog at https://scikit-learn.org/dev/whats_new/v1.0.html

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- update to 0.24.2:
  * a lot of bugfixes see https://scikit-learn.org/stable/whats_new/v0.24.html
- drop scikit-learn-pr19101-npfloat.patch: upstream

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- Add assert_allclose-for-FP-comparison.patch to overcome
  equality comparison for FP numbers
  (gh#scikit-learn/scikit-learn#18031).

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