MAINT Parameter Validation for feature_selection.f_classif (#25720)

ZeeshanLone · jeremiedbb · web-flow · commit 7e15694559e6 · 2023-02-28T14:01:04.000+01:00
Co-authored-by: Jérémie du Boisberranger &lt;34657725+jeremiedbb@users.noreply.github.com&gt;
diff --git a/sklearn/feature_selection/_univariate_selection.py b/sklearn/feature_selection/_univariate_selection.py
@@ -117,6 +117,12 @@ def f_oneway(*args):
     return f, prob
 
 
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+    }
+)
 def f_classif(X, y):
     """Compute the ANOVA F-value for the provided sample.
 
@@ -127,7 +133,7 @@ def f_classif(X, y):
     X : {array-like, sparse matrix} of shape (n_samples, n_features)
         The set of regressors that will be tested sequentially.
 
-    y : ndarray of shape (n_samples,)
+    y : array-like of shape (n_samples,)
         The target vector.
 
     Returns
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -113,6 +113,7 @@ def _check_function_param_validation(
     "sklearn.feature_extraction.image.extract_patches_2d",
     "sklearn.feature_extraction.image.reconstruct_from_patches_2d",
     "sklearn.feature_selection.chi2",
+    "sklearn.feature_selection.f_classif",
     "sklearn.metrics.accuracy_score",
     "sklearn.metrics.auc",
     "sklearn.metrics.average_precision_score",
