8000 ENH add support for sample_weight in KBinsDiscretizer(strategy="quantile") by DeaMariaLeon · Pull Request #1 · DeaMariaLeon/scikit-learn · GitHub
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Reference Issues/PRs

Fixes scikit-learn#22048

What does this implement/fix?

Adding support for sample_weight in KBinsDiscretizer(strategy="quantile"). That is to say : I added a parameter in the fit method of KBinsDiscretizer. This parameter, sample_weight can be set to control the weights associated to each sample only when the strategy is set to "quantile"

Seladus and others added 30 commits December 21, 2021 13:58
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
… statement and adding a test case for the exception raise on this same check
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
…earn#24354)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com>
… `NeighborsBase` (scikit-learn#24750)

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
thomasjpfan and others added 17 commits November 11, 2022 11:10
…ing.html' (scikit-learn#24892)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Tig <tionni>
…learn#24889)

* Allow categorical_features to be passed as feature names

* Update relevant examples

* Call .set_output(transform='pandas') on the pipelines
Merge remote-tracking branch 'upstream/main' into deas-kbins
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