Random forest (machine learning)
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At the time the article was created Candace Makeda Moore had no recorded disclosures.View Candace Makeda Moore's current disclosures
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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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