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The first algorithm for random decision forests was created in 1995 by Tin Kam Ho [1] using the random subspace method, [2] which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.
When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each sample is only considered out-of-bag for the trees that do not include it in their bootstrap sample.
Pros and Cons of Random Forests and Bagging Pros Cons There are overall less requirements involved for normalization and scaling, making the use of random forests more convenient. [8] The algorithm may change significantly if there is a slight change to the data being bootstrapped and used within the forests. [9]
The random subspace method has been used for decision trees; when combined with "ordinary" bagging of decision trees, the resulting models are called random forests. [5] It has also been applied to linear classifiers, [6] support vector machines, [7] nearest neighbours [8] [9] and other types of classifiers.
In statistics, jackknife variance estimates for random forest are a way to estimate the variance in random forest models, in order to eliminate the bootstrap effects.
Random forest, a machine-learning classifier based on choosing random subsets of variables for each tree and using the most frequent tree output as the overall classification Branching process , a model of a population in which each individual has a random number of children
Rotation forest – in which every decision tree is trained by first applying principal component analysis (PCA) on a random subset of the input features. [ 13 ] A special case of a decision tree is a decision list , [ 14 ] which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a ...
The section also claims that "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." implying that it is not appropriate for continuous features, which is simply false. Nippashish 07:22, 16 March 2013 (UTC)