Random forest (machine learning)

Random Forest also known as random decision forests are a specific type of ensembling algorithm that utilizes a combination of decision trees based on subsets of a dataset. A random forest algorithm does not make a decision tree of smaller decision trees, but rather utilizes decision trees in parallel for prediction. Random forest algorithms are typically more accurate than single decision trees although less intuitive.

A specific type of random forest algorithm that utilized bagging was originally coded into a software package and registered with a trademark under the name Random Forests by Leo Breiman and Adele Cutler 1

Random forest algorithms have been used for classification tasks including diagnostic, prognostic 2 and segmentation tasks in radiology. Random forest algorithms can also be applied to regression and other statistical problems.

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