Fixed _calc_score for *scikit-learn* version compatibility (#1109)

* added sklearn version compability for fit_params

* added change to changelog

* fixed format

* fixed order of imports

* formatted with isort and black

docs/sources/CHANGELOG.md

@@ -23,6 +23,8 @@ Files updated:
   - ['mlxtend.frequent_patterns.fpcommon']
   - ['mlxtend.frequent_patterns.fpgrowth'](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpgrowth/)
   - ['mlxtend.frequent_patterns.fpmax'](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpmax/)
+  - ['mlxtend/feature_selection/utilities.py'](https://github.com/rasbt/mlxtend/blob/master/mlxtend/feature_selection/utilities.py)
+      - Modified `_calc_score` function to ensure compatibility with *scikit-learn* versions 1.4 and above by dynamically selecting between `fit_params` and `params` in `cross_val_score`.
   - [`mlxtend.feature_selection.SequentialFeatureSelector`](https://github.com/rasbt/mlxtend/blob/master/mlxtend/feature_selection/sequential_feature_selector.py)
     - Updated negative infinity constant to be compatible with old and new (>=2.0) `numpy` versions
   - [`mlxtend.frequent_patterns.association_rules`](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/)

mlxtend/feature_selection/utilities.py

@@ -1,6 +1,5 @@
-from copy import deepcopy
-
 import numpy as np
+from sklearn import __version__ as sklearn_version
 from sklearn.model_selection import cross_val_score
 
 
@@ -94,6 +93,9 @@ def _calc_score(
         feature_groups = [[i] for i in range(X.shape[1])]
 
     IDX = _merge_lists(feature_groups, indices)
+
+    param_name = "fit_params" if sklearn_version < "1.4" else "params"
+
     if selector.cv:
         scores = cross_val_score(
             selector.est_,
@@ -104,7 +106,7 @@ def _calc_score(
             scoring=selector.scorer,
             n_jobs=1,
             pre_dispatch=selector.pre_dispatch,
-            fit_params=fit_params,
+            **{param_name: fit_params},
         )
     else:
         selector.est_.fit(X[:, IDX], y, **fit_params)