Random forests, or random decision forests, are an ensemble learning method for classification, regression and other tasks that works by constructing a multitude of decision trees during training. For a classification task the forest's output is the class chosen by the largest number of trees; for regression it is the average of the individual trees' predictions, correcting for a single decision tree's tendency to overfit its training set. The first random forest algorithm was developed by Tin Kam Ho in 1995 using the random subspace method, and Leo Breiman and Adele Cutler substantially extended it, with Breiman's 2001 paper becoming the method's standard reference. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/
Facts
Classification
Design Technique Credited ToTin Kam Ho, 1995 (random subspace method); extended by Leo Breiman and Adele Cutler, 2001. 1 Connections
Credited To
Source Wikipedia: Random Forest Algorithm
In Field
Source Wikipedia: Random Forest Algorithm
Invented By
Source Wikipedia: Random Forest Algorithm
Preceded By
Leo Breiman introduced bootstrap aggregating (bagging) in 1996 and built directly on it in his 2001 random forest algorithm.
Source Wikipedia: Random Forest Algorithm
Uses Design Technique
Sources
1. Wikipedia: Random Forest Algorithm
Wikimedia FoundationLead section
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of the trees.
Lead section
The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg. An extension of the algorithm was developed by Leo Breiman and Adele Cutler, who registered "Random Forests" as a trademark in 2006 (as of 2019, owned by Minitab, Inc.).
entity record, description (design-technique)
The first random forest algorithm was developed by Tin Kam Ho in 1995 using the random subspace method, and Leo Breiman and Adele Cutler substantially extended it, with Breiman's 2001 paper becoming the method's standard reference.
Preceded By: Bootstrap Aggregating Algorithm, Bagging section
The training algorithm for random forests applies the general technique of bootstrap aggregating, or bagging, to tree learners.
In Field: Machine Learning, Lead
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training.
In Group: Decision Tree and Ensemble Learning Algorithms, Lead
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training.
Credited To: Tin Kam Ho, Lead
The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.
Invented By: Tin Kam Ho, Lead
The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.
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