DOC Fix typos in ensemble docs
@@ -562,7 +562,7 @@ for regression which can be specified via the argument
The figure below shows the results of applying :class:`GradientBoostingRegressor`
with least squares loss and 500 base learners to the diabetes dataset
(:func:`sklearn.datasets.load_diabetes`).
-The plot on the left shows the train and test error at each iteration.
+The plot shows the train and test error at each iteration.
The train error at each iteration is stored in the
:attr:`~GradientBoostingRegressor.train_score_` attribute
of the gradient boosting model. The test error at each iterations can be obtained
@@ -979,7 +979,7 @@ corresponds to :math:`\lambda` in equation (2) of [XGBoost]_.
Note that **early-stopping is enabled by default if the number of samples is
larger than 10,000**. The early-stopping behaviour is controlled via the
-``early-stopping``, ``scoring``, ``validation_fraction``,
+``early_stopping``, ``scoring``, ``validation_fraction``,
``n_iter_no_change``, and ``tol`` parameters. It is possible to early-stop
using an arbitrary :term:`scorer`, or just the training or validation loss.
Note that for technical reasons, using a scorer is significantly slower than
