Introduction - If you have any usage issues, please Google them yourself
Random Forest classification or regression trees by the multi-component. The main idea comes the Bagging algorithm, Bagging technology thinking mainly given a set of weak classifiers and training, so that the learning algorithm to train several rounds, each round of training set by the original training set is randomly selected with replacement, with the original size of the general training set fairly, and in turn train a plurality of weak classifiers, the final classification by the weak classifier combination of these, for the general classification of a majority voting method, commonly used for regression simple average method. Random forests bagging on the basis of each of the weak classifiers are decision trees, decision tree in the generation process, the choice of property on the increase in the probability of selection according to certain attributes, and choose the best attributes of these properties in the split point traditional practice is generally to choose the best of