DOC Specify shape with parentheses in ensemble user guide
@@ -110,9 +110,9 @@ construction. The prediction of the ensemble is given as the averaged
prediction of the individual classifiers.
As other classifiers, forest classifiers have to be fitted with two
-arrays: a sparse or dense array X of size ``[n_samples, n_features]`` holding the
-training samples, and an array Y of size ``[n_samples]`` holding the
-target values (class labels) for the training samples::
+arrays: a sparse or dense array X of shape ``(n_samples, n_features)``
+holding the training samples, and an array Y of shape ``(n_samples,)``
+holding the target values (class labels) for the training samples::
>>> from sklearn.ensemble import RandomForestClassifier
>>> X = [[0, 0], [1, 1]]
@@ -120,9 +120,9 @@ target values (class labels) for the training samples::
>>> clf = RandomForestClassifier(n_estimators=10)
>>> clf = clf.fit(X, Y)
-Like :ref:`decision trees <tree>`, forests of trees also extend
-to :ref:`multi-output problems <tree_multioutput>` (if Y is an array of size
-``[n_samples, n_outputs]``).
+Like :ref:`decision trees <tree>`, forests of trees also extend to
+:ref:`multi-output problems <tree_multioutput>` (if Y is an array
+of shape ``(n_samples, n_outputs)``).
Random Forests
--------------
