Pushing the docs to dev/ for branch: main, commit 36c50736017aeb114f9586b1e1b56779dee03749

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: d8d93d58d77af804231b3c068f3af569
+config: 2b16a0cc05384bab61484a373056d699
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -447,7 +447,7 @@ def get_metadata_routing(self):
         return router
 
     def fit(self, X, y, **fit_params):
-        params = process_routing(self, "fit", fit_params)
+        params = process_routing(self, "fit", **fit_params)
 
         self.transformer_ = clone(self.transformer).fit(X, y, **params.transformer.fit)
         X_transformed = self.transformer_.transform(X, **params.transformer.transform)
@@ -458,7 +458,7 @@ def fit(self, X, y, **fit_params):
         return self
 
     def predict(self, X, **predict_params):
-        params = process_routing(self, "predict", predict_params)
+        params = process_routing(self, "predict", **predict_params)
 
         X_transformed = self.transformer_.transform(X, **params.transformer.transform)
         return self.classifier_.predict(X_transformed, **params.classifier.predict)
@@ -543,7 +543,7 @@ def __init__(self, estimator):
         self.estimator = estimator
 
     def fit(self, X, y, **fit_params):
-        params = process_routing(self, "fit", fit_params)
+        params = process_routing(self, "fit", **fit_params)
         self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
 
     def get_metadata_routing(self):
@@ -572,7 +572,7 @@ def __init__(self, estimator):
         self.estimator = estimator
 
     def fit(self, X, y, sample_weight=None, **fit_params):
-        params = process_routing(self, "fit", fit_params, sample_weight=sample_weight)
+        params = process_routing(self, "fit", sample_weight=sample_weight, **fit_params)
         check_metadata(self, sample_weight=sample_weight)
         self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)