scikit-learn
diff --git a/‎sklearn/ensemble/_gb.py
Lines changed: 11 additions & 11 deletions b/‎sklearn/ensemble/_gb.py
Lines changed: 11 additions & 11 deletions
@@ -622,7 +622,7 @@ def _fit_stages(
                 X_csr,
             )
 
-            # track deviance (= loss)
+            # track loss
             if do_oob:
                 self.train_score_[i] = loss_(
                     y[sample_mask],
@@ -1056,28 +1056,28 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
         :func:`sklearn.inspection.permutation_importance` as an alternative.
 
     oob_improvement_ : ndarray of shape (n_estimators,)
-        The improvement in loss (= deviance) on the out-of-bag samples
+        The improvement in loss on the out-of-bag samples
         relative to the previous iteration.
         ``oob_improvement_[0]`` is the improvement in
         loss of the first stage over the ``init`` estimator.
         Only available if ``subsample < 1.0``.
 
     oob_scores_ : ndarray of shape (n_estimators,)
-        The full history of the loss (= deviance) values on the out-of-bag
+        The full history of the loss values on the out-of-bag
         samples. Only available if `subsample < 1.0`.
 
         .. versionadded:: 1.3
 
     oob_score_ : float
-        The last value of the loss (= deviance) on the out-of-bag samples. It is
+        The last value of the loss on the out-of-bag samples. It is
         the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.
 
         .. versionadded:: 1.3
 
     train_score_ : ndarray of shape (n_estimators,)
-        The i-th score ``train_score_[i]`` is the deviance (= loss) of the
+        The i-th score ``train_score_[i]`` is the loss of the
         model at iteration ``i`` on the in-bag sample.
-        If ``subsample == 1`` this is the deviance on the training data.
+        If ``subsample == 1`` this is the loss on the training data.
 
     init_ : estimator
         The estimator that provides the initial predictions.
@@ -1619,28 +1619,28 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
         :func:`sklearn.inspection.permutation_importance` as an alternative.
 
     oob_improvement_ : ndarray of shape (n_estimators,)
-        The improvement in loss (= deviance) on the out-of-bag samples
+        The improvement in loss on the out-of-bag samples
         relative to the previous iteration.
         ``oob_improvement_[0]`` is the improvement in
         loss of the first stage over the ``init`` estimator.
         Only available if ``subsample < 1.0``.
 
     oob_scores_ : ndarray of shape (n_estimators,)
-        The full history of the loss (= deviance) values on the out-of-bag
+        The full history of the loss values on the out-of-bag
         samples. Only available if `subsample < 1.0`.
 
         .. versionadded:: 1.3
 
     oob_score_ : float
-        The last value of the loss (= deviance) on the out-of-bag samples. It is
+        The last value of the loss on the out-of-bag samples. It is
         the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`.
 
         .. versionadded:: 1.3
 
     train_score_ : ndarray of shape (n_estimators,)
-        The i-th score ``train_score_[i]`` is the deviance (= loss) of the
+        The i-th score ``train_score_[i]`` is the loss of the
         model at iteration ``i`` on the in-bag sample.
-        If ``subsample == 1`` this is the deviance on the training data.
+        If ``subsample == 1`` this is the loss on the training data.
 
     init_ : estimator
         The estimator that provides the initial predictions.