scikit-learn
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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+4-5Lines changed: 4 additions & 5 deletions b/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
Copy file name to clipboardExpand all lines: sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
+4-5Lines changed: 4 additions & 5 deletions
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
Copy file name to clipboardExpand all lines: sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
+2-2Lines changed: 2 additions & 2 deletions b/‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
Copy file name to clipboardExpand all lines: sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
+2-2Lines changed: 2 additions & 2 deletions
@@ -82,7 +82,7 @@ def _validate_parameters(self):
             raise ValueError(
                 'validation_fraction={} must be strictly '
                 'positive, or None.'.format(self.validation_fraction))
-        if self.tol is not None and self.tol < 0:
+        if self.tol < 0:
             raise ValueError('tol={} '
                              'must not be smaller than 0.'.format(self.tol))
 
@@ -646,8 +646,7 @@ def _should_stop(self, scores):
         # harder for subsequent iteration to be considered an improvement upon
         # the reference score, and therefore it is more likely to early stop
         # because of the lack of significant improvement.
-        tol = 0 if self.tol is None else self.tol
-        reference_score = scores[-reference_position] + tol
+        reference_score = scores[-reference_position] + self.tol
         recent_scores = scores[-reference_position + 1:]
         recent_improvements = [score > reference_score
                                for score in recent_scores]
@@ -992,7 +991,7 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
         stopped when none of the last ``n_iter_no_change`` scores are better
         than the ``n_iter_no_change - 1`` -th-to-last one, up to some
         tolerance. Only used if early stopping is performed.
-    tol : float or None, default=1e-7
+    tol : float, default=1e-7
         The absolute tolerance to use when comparing scores during early
         stopping. The higher the tolerance, the more likely we are to early
         stop: higher tolerance means that it will be harder for subsequent
@@ -1245,7 +1244,7 @@ class HistGradientBoostingClassifier(ClassifierMixin,
         stopped when none of the last ``n_iter_no_change`` scores are better
         than the ``n_iter_no_change - 1`` -th-to-last one, up to some
         tolerance. Only used if early stopping is performed.
-    tol : float or None, default=1e-7
+    tol : float, default=1e-7
         The absolute tolerance to use when comparing scores. The higher the
         tolerance, the more likely we are to early stop: higher tolerance
         means that it will be harder for subsequent iterations to be
 
@@ -85,7 +85,7 @@ def test_invalid_classification_loss():
         (None, None, True, 5, 1e-1),
         ('loss', .1, True, 5, 1e-7),  # use loss
         ('loss', None, True, 5, 1e-1),  # use loss on training data
-        (None, None, False, 5, None),  # no early stopping
+        (None, None, False, 5, 0.0),  # no early stopping
         ])
 def test_early_stopping_regression(scoring, validation_fraction,
                                    early_stopping, n_iter_no_change, tol):
@@ -126,7 +126,7 @@ def test_early_stopping_regression(scoring, validation_fraction,
         (None, None, True, 5, 1e-1),
         ('loss', .1, True, 5, 1e-7),  # use loss
         ('loss', None, True, 5, 1e-1),  # use loss on training data
-        (None, None, False, 5, None),  # no early stopping
+        (None, None, False, 5, 0.0),  # no early stopping
         ])
 def test_early_stopping_classification(data, scoring, validation_fraction,
                                        early_stopping, n_iter_no_change, tol):