[BUG] fix BaseRegressor.score method failing with `sklearn.metrics …

…r2_score got an unexpected keyword argument 'normalize'` (sktime#6019)

#### Reference Issues/PRs
Fixes sktime#6018

#### What does this implement/fix? Explain your changes.
Remove non-existing `normalize` parameter for `r2_score` in
`sktime.regression.base` class : `BaseRegressor` method : `score`.

.all-contributorsrc

@@ -2590,7 +2590,9 @@
       "avatar_url": "https://avatars.githubusercontent.com/u/69190238?v=4",
       "profile": "https://cyrilmeyer.eu/",
       "contributions": [
-        "code"
+        "bug",
+        "code",
+        "test"
       ]
     },
     {

sktime/regression/base.py

@@ -299,7 +299,7 @@ def score(self, X, y, multioutput="uniform_average") -> float:
 
         self.check_is_fitted()
 
-        return r2_score(y, self.predict(X), normalize=True, multioutput=multioutput)
+        return r2_score(y, self.predict(X), multioutput=multioutput)
 
     def _fit(self, X, y):
         """Fit time series regressor to training data.

sktime/regression/tests/test_all_regressors.py

@@ -77,6 +77,9 @@ def test_regressor_output(self, estimator_instance, scenario):
 
         # run fit and predict
         y_pred = scenario.run(estimator_instance, method_sequence=["fit", "predict"])
+        # check score
+        score = estimator_instance.score(X_new, y_pred)
+        assert np.issubdtype(score.dtype, np.floating)
 
         # check predict
         assert isinstance(y_pred, np.ndarray)
@@ -95,6 +98,9 @@ def test_multioutput(self, estimator_instance):
 
         estimator_instance.fit(X, y_mult)
         y_pred = estimator_instance.predict(X)
+        # check score
+        score = estimator_instance.score(X, y_mult)
+        assert np.issubdtype(score.dtype, np.floating)
 
         assert isinstance(y_pred, pd.DataFrame)
         assert y_pred.shape == y_mult.shape