A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
Trees in the forest use the best split strategy, i.e. equivalent to passing
`splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
For a comparison between tree-based ensemble models see the example
:ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.