- Update to 1.4.1.post1
## Metadata Routing
* Fix routing issue with ColumnTransformer when used inside
another meta-estimator. #28188 by Adrin Jalali.
* No error is raised when no metadata is passed to a
metaestimator that includes a sub-estimator which doesn’t
support metadata routing. #28256 by Adrin Jalali.
* Fix multioutput.MultiOutputRegressor and
multioutput.MultiOutputClassifier to work with estimators that
don’t consume any metadata when metadata routing is enabled.
#28240 by Adrin Jalali.
## DataFrame Support
* Enhancement Fix Pandas and Polars dataframe are validated
directly without ducktyping checks. #28195 by Thomas Fan.
## Changes impacting many modules
* Efficiency Fix Partial revert of #28191 to avoid a performance
regression for estimators relying on euclidean pairwise
computation with sparse matrices. The impacted estimators are:
- 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
- #28235 by Julien Jerphanion.
* Fixes a bug for all scikit-learn transformers when using
set_output with transform set to pandas or polars. The bug
could lead to wrong naming of the columns of the returned
dataframe. #28262 by Guillaume Lemaitre.
* When users try to use a method in StackingClassifier,
StackingClassifier, StackingClassifier, SelectFromModel, RFE,
SelfTrainingClassifier, OneVsOneClassifier,
OutputCodeClassifier or OneVsRestClassifier that their
sub-estimators don’t implement, the AttributeError now reraises
in the traceback. #28167 by Stefanie Senger.
- Release 1.4.0
* HistGradientBoosting Natively Supports Categorical DTypes in
DataFrames
* Polars output in set_output
* Missing value support for Random Forest
* Add support for monotonic constraints in tree-based models
* Enriched estimator displays
* Metadata Routing Support
* Improved memory and runtime efficiency for PCA on sparse data
* Highlights and detailed changelog:
* https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
* https://scikit-learn.org/stable/whats_new/v1.4.html#release-notes-1-4
- Enable python312 test flavor, avoid testing it with the other
flavors
- Prepare for python39 flavor drop