Random Forest
Random forest (or random forests) is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the classes output by individual trees. The algorithm for inducing a random forest was developed by Leo Breiman and Adele Cutler, and "Random Forests" is their trademark. The term came from random decision forests that was first proposed by Tin Kam Ho of Bell Labs in 1995. The method combines Breiman's "bagging" idea and the random selection of features, introduced independently by Ho and Amit and Geman in order to construct a collection of decision trees with controlled variation.
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Some articles on random forest:
... Generalizing Random Forest to Naive Bayes, Random Naive Bayes (Random NB), is a bagged classifier combining a forest of B Naive Bayes ... put the input vector down the B Naive Bayes in the forest ... Unlike Random Forest, the predicted class of the ensemble is assessed by adjusted majority voting rather than majority voting, as each bth Naive Bayes ...
... In order to form an intuitive visualization of the model-space represented by a random forest, a dataset consisting of 200 random points (100 green points and 100 red points) was created ... A Random Forest model, consisting of 50 trees, was trained on this data ... (Typically, random forest is best-suited for use with categorical features, but continuous features were used in this illustration because they were easier to visualize.) ...
Famous quotes containing the words forest and/or random:
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—Edward Dahlberg (1900–1977)
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