scikit-learn
diff --git a/‎sklearn/ensemble/_forest.py‎
Lines changed: 1 addition & 2 deletions b/‎sklearn/ensemble/_forest.py‎
Lines changed: 1 addition & 2 deletions
diff --git a/‎sklearn/ensemble/_gb.py‎
Lines changed: 2 additions & 2 deletions b/‎sklearn/ensemble/_gb.py‎
Lines changed: 2 additions & 2 deletions
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py‎
Lines changed: 3 additions & 3 deletions b/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py‎
Lines changed: 3 additions & 3 deletions
diff --git a/‎sklearn/tree/_classes.py‎
Lines changed: 3 additions & 4 deletions b/‎sklearn/tree/_classes.py‎
Lines changed: 3 additions & 4 deletions
@@ -2061,8 +2061,7 @@ class ExtraTreesRegressor(ForestRegressor):
            The default value of ``n_estimators`` changed from 10 to 100
            in 0.22.
 
-    criterion : {"squared_error", "mse", "absolute_error", "mae"}, \
-            default="squared_error"
+    criterion : {"squared_error", "absolute_error"}, default="squared_error"
         The function to measure the quality of a split. Supported criteria
         are "squared_error" for the mean squared error, which is equal to
         variance reduction as feature selection criterion, and "absolute_error"
 
@@ -1474,8 +1474,8 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
 
     Parameters
     ----------
-    loss : {'squared_error', 'ls', 'absolute_error', 'lad', 'huber', \
-            'quantile'}, default='squared_error'
+    loss : {'squared_error', 'absolute_error', 'huber', 'quantile'}, \
+            default='squared_error'
         Loss function to be optimized. 'squared_error' refers to the squared
         error for regression. 'absolute_error' refers to the absolute error of
         regression and is a robust loss function. 'huber' is a
 
@@ -1021,10 +1021,10 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
 
     Parameters
     ----------
-    loss : {'squared_error', 'least_squares', 'absolute_error', \
-            'least_absolute_deviation', 'poisson'}, default='squared_error'
+    loss : {'squared_error', 'absolute_error', 'poisson'}, \
+            default='squared_error'
         The loss function to use in the boosting process. Note that the
-        "least squares" and "poisson" losses actually implement
+        "squared error" and "poisson" losses actually implement
         "half least squares loss" and "half poisson deviance" to simplify the
         computation of the gradient. Furthermore, "poisson" loss internally
         uses a log-link and requires ``y >= 0``.
 
@@ -1038,8 +1038,8 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
 
     Parameters
     ----------
-    criterion : {"squared_error", "mse", "friedman_mse", "absolute_error", \
-            "mae", "poisson"}, default="squared_error"
+    criterion : {"squared_error", "friedman_mse", "absolute_error", \
+            "poisson"}, default="squared_error"
         The function to measure the quality of a split. Supported criteria
         are "squared_error" for the mean squared error, which is equal to
         variance reduction as feature selection criterion and minimizes the L2
@@ -1630,8 +1630,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
 
     Parameters
     ----------
-    criterion : {"squared_error", "mse", "friedman_mse", "mae"}, \
-            default="squared_error"
+    criterion : {"squared_error", "friedman_mse"}, default="squared_error"
         The function to measure the quality of a split. Supported criteria
         are "squared_error" for the mean squared error, which is equal to
         variance reduction as feature selection criterion and "mae" for the