DOC: add description for 'train_sizes' in examples.model_selection.plot_learning_curve
@@ -65,6 +65,16 @@ def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
+
+ train_sizes : array-like, shape (n_ticks,), dtype float or int
+ Relative or absolute numbers of training examples that will be used to
+ generate the learning curve. If the dtype is float, it is regarded as a
+ fraction of the maximum size of the training set (that is determined
+ by the selected validation method), i.e. it has to be within (0, 1].
+ Otherwise it is interpreted as absolute sizes of the training sets.
+ Note that for classification the number of samples usually have to
+ be big enough to contain at least one sample from each class.
+ (default: np.linspace(0.1, 1.0, 5))
"""
plt.figure()
plt.title(title)
