fix: *sklearn* version compability in stacking_cv params (#1124)

* fix: update parameter handling for sklearn version compatibility in stacking_cv

* added fix to changelog

* removed trailing whitespaces for flake8

Author: d-kleine

docs/sources/CHANGELOG.md

@@ -7,6 +7,11 @@ The CHANGELOG for the current development version is available at
 
 ---
 
+##### Changes
+
+  - [`mlxtend/classifier/stacking_cv_classification.py`](https://github.com/rasbt/mlxtend/blob/master/mlxtend/classifier/stacking_cv_classification.py) and [`mlxtend/regressor/stacking_cv_regression.py`](https://github.com/rasbt/mlxtend/blob/master/mlxtend/regressor/stacking_cv_regression.py)
+      - Modified `meta_features` to ensure compatibility with *scikit-learn* versions 1.4 and above by dynamically selecting between `fit_params` and `params` in `cross_val_predict`.
+
 ### Version 0.24.4  (25 Nov 2025)
 
 ##### Downloads

mlxtend/classifier/stacking_cv_classification.py

@@ -11,6 +11,7 @@
 
 import numpy as np
 from scipy import sparse
+from sklearn import __version__ as sklearn_version
 from sklearn.base import TransformerMixin, clone
 from sklearn.model_selection import cross_val_predict
 from sklearn.model_selection._split import check_cv
@@ -266,14 +267,15 @@ def fit(self, X, y, groups=None, sample_weight=None):
             if self.verbose > 1:
                 print(_name_estimators((model,))[0][1])
 
+            param_name = "fit_params" if sklearn_version < "1.4" else "params"
             prediction = cross_val_predict(
                 model,
                 X,
                 y,
                 groups=groups,
                 cv=final_cv,
                 n_jobs=self.n_jobs,
-                fit_params=fit_params,
+                **{param_name: fit_params},
                 verbose=self.verbose,
                 pre_dispatch=self.pre_dispatch,
                 method="predict_proba" if self.use_probas else "predict",

mlxtend/regressor/stacking_cv_regression.py

@@ -15,6 +15,7 @@
 
 import numpy as np
 from scipy import sparse
+from sklearn import __version__ as sklearn_version
 from sklearn.base import RegressorMixin, TransformerMixin, clone
 from sklearn.model_selection import cross_val_predict
 from sklearn.model_selection._split import check_cv
@@ -211,6 +212,7 @@ def fit(self, X, y, groups=None, sample_weight=None):
             fit_params = None
         else:
             fit_params = dict(sample_weight=sample_weight)
+        param_name = "fit_params" if sklearn_version < "1.4" else "params"
         meta_features = np.column_stack(
             [
                 cross_val_predict(
@@ -221,7 +223,7 @@ def fit(self, X, y, groups=None, sample_weight=None):
                     cv=kfold,
                     verbose=self.verbose,
                     n_jobs=self.n_jobs,
-                    fit_params=fit_params,
+                    **{param_name: fit_params},
                     pre_dispatch=self.pre_dispatch,
                 )
                 for regr in self.regr_