[dependabot]

Updates the requirements on category-encoders to permit the latest version.

Changelog

Sourced from category-encoders's changelog.

v.2.11.2

• Fix: composite_cols no longer raises the "separator occurs in the data" ValueError for column groups containing missing values on pandas >= 3, where astype(str) keeps missing values as NaN. The separator-collision check now runs on complete rows only; behavior on pandas 2 is unchanged (#528).
• Fix: CountEncoder now clears its learned rare-category groups at the start of each fit, so refitting (including refitting a pipeline) with min_group_size no longer applies a stale grouping from the previous fit (#529).
• Chore: bumped certifi and urllib3 in poetry.lock (#551, #535).

v.2.11.1

• Fix: NestedCVWrapper.fit_transform now returns out-of-fold rows in the input's original order (issue#526). Previously the per-fold encodings were concatenated in cross-validation iteration order, so with a shuffling splitter (including the wrapper's default StratifiedKFold) positional consumers (NumPy arrays, .iloc, scikit-learn estimators) received rows misaligned with y even though the index labels were correct.

v.2.11.0

• Docs: Fixed issue#400 - OneHotEncoder's handle_missing='value' docstring wrongly claimed missing values are zero-filled; corrected it to describe the actual (and, per test_missing_values, intended library-wide) behavior of treating a missing value seen during fit as its own category, same as MultiHotEncoder's existing value/ignore split. No behavior change: the existing ignore option already zero-fills missing values.
• Docs: Added a documentation page on how category_encoders differs from scikit-learn conventions (issue#480).
• Feat: Added handle_missing='ignore' to RankHotEncoder, matching the option already available on OneHotEncoder and MultiHotEncoder (issue#400's RankHotEncoder gap). Zero-fills a missing value at both fit and transform time, without adding an extra thermometer column for it; the default value behavior (own category, unchanged) still matches test_missing_values' library-wide invariant.
• Feat: Added issue#420 - CountTargetEncoder, a supervised count-based encoder that stores per-class category counts and encodes categories as smoothing-adjusted log-odds against the global target prior (binary targets emit one WOE-style column, multiclass targets one column per class). Classification-only for now; a binning-based regression variant is planned as a follow-up.
• Feat: Added MultiHotEncoder, an unsupervised encoder for delimiter-separated multi-value cells (e.g. 'mathematics|physics'): every distinct item seen at fit gets one binary column, with configurable delimiter and the usual handle_unknown/handle_missing support (issue#161).
• Feat: Added composite_cols on supervised encoders (Target, MEstimate, WOE, JamesStein, CatBoost, GLMM, LeaveOneOut, Quantile): joint ("composite") target encoding of column groups passed as tuples, learning statistics over

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