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Fixed grammar #1121

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Mar 1, 2025
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Fixed grammar #1121

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12 changes: 6 additions & 6 deletions doc/ensemble.rst
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Expand Up @@ -19,7 +19,7 @@ Bagging classifier
In ensemble classifiers, bagging methods build several estimators on different
randomly selected subset of data. In scikit-learn, this classifier is named
:class:`~sklearn.ensemble.BaggingClassifier`. However, this classifier does not
allow to balance each subset of data. Therefore, when training on imbalanced
allow each subset of data to be balanced. Therefore, when training on an imbalanced
data set, this classifier will favor the majority classes::

>>> from sklearn.datasets import make_classification
Expand Down Expand Up @@ -59,10 +59,10 @@ sampling is controlled by the parameter `sampler` or the two parameters
>>> balanced_accuracy_score(y_test, y_pred)
0.8...

Changing the `sampler` will give rise to different known implementation
Changing the `sampler` will give rise to different known implementations
:cite:`maclin1997empirical`, :cite:`hido2009roughly`,
:cite:`wang2009diversity`. You can refer to the following example shows in
practice these different methods:
:cite:`wang2009diversity`. You can refer to the following example which shows these
different methods in practice:
:ref:`sphx_glr_auto_examples_ensemble_plot_bagging_classifier.py`

.. _forest:
Expand Down Expand Up @@ -93,7 +93,7 @@ Boosting

Several methods taking advantage of boosting have been designed.

:class:`RUSBoostClassifier` randomly under-sample the dataset before to perform
:class:`RUSBoostClassifier` randomly under-samples the dataset before performing
a boosting iteration :cite:`seiffert2009rusboost`::

>>> from imblearn.ensemble import RUSBoostClassifier
Expand All @@ -107,7 +107,7 @@ a boosting iteration :cite:`seiffert2009rusboost`::

A specific method which uses :class:`~sklearn.ensemble.AdaBoostClassifier` as
learners in the bagging classifier is called "EasyEnsemble". The
:class:`EasyEnsembleClassifier` allows to bag AdaBoost learners which are
:class:`EasyEnsembleClassifier` allows bagging AdaBoost learners which are
trained on balanced bootstrap samples :cite:`liu2008exploratory`. Similarly to
the :class:`BalancedBaggingClassifier` API, one can construct the ensemble as::

Expand Down