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TESTa7984d75aesklearn.ensemble._forest.RandomForestClassifier

TESTa7984d75aesklearn.ensemble._forest.RandomForestClassifier

Visibility: public Uploaded 18-10-2024 by Continuous Integration sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 0 runs
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  • openml-python python scikit-learn sklearn sklearn_1.5.2
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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`.

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