Description: In Random Forest, we’ve collection of decision trees (so known as “Forest”). To classify a new object based on attributes, each tree gives a classification and we say the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).
Random Forest is a trademark term for an ensemble of decision trees. Decision trees works for both categorical and continuous dependent variables.
Algorithm: Each tree is planted & grown as follows:
1. If the number of cases in the training set is N, then sample of N cases is taken at random but with replacement. This sample will be the training set for growing the tree. 2. If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m is held constant during the forest growing. 3. Each tree is grown to the largest extent possible. There is no pruning.
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