scikit-learn · thomasjpfan · Mar 28, 2023 · Mar 24, 2023
diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py
@@ -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.