DOC Specify shape with parentheses in decision tree guide
@@ -91,8 +91,8 @@ Classification
classification on a dataset.
As with other classifiers, :class:`DecisionTreeClassifier` takes as input two arrays:
-an array X, sparse or dense, of size ``[n_samples, n_features]`` holding the
-training samples, and an array Y of integer values, size ``[n_samples]``,
+an array X, sparse or dense, of shape ``(n_samples, n_features)`` holding the
+training samples, and an array Y of integer values, shape ``(n_samples,)``,
holding the class labels for the training samples::
>>> from sklearn import tree
@@ -246,7 +246,7 @@ Multi-output problems
=====================
A multi-output problem is a supervised learning problem with several outputs
-to predict, that is when Y is a 2d array of size ``[n_samples, n_outputs]``.
+to predict, that is when Y is a 2d array of shape ``(n_samples, n_outputs)``.
When there is no correlation between the outputs, a very simple way to solve
this kind of problem is to build n independent models, i.e. one for each
@@ -267,7 +267,7 @@ multi-output problems. This requires the following changes:
This module offers support for multi-output problems by implementing this
strategy in both :class:`DecisionTreeClassifier` and
:class:`DecisionTreeRegressor`. If a decision tree is fit on an output array Y
-of size ``[n_samples, n_outputs]`` then the resulting estimator will:
+of shape ``(n_samples, n_outputs)`` then the resulting estimator will:
* Output n_output values upon ``predict``;
