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| ||This class implements the Random Forests algorithm. In Random Forests algorithm, we train a number of randomized CART trees (see class CRandomCARTree) using the supplied training data. The number of trees to be trained is a parameter (called number of bags) controlled by the user. Test feature vectors are classified/regressed by combining the outputs of all these trained candidate trees using a combination rule (see class CCombinationRule). The feature for calculating out-of-box error is also provided to help determine the appropriate number of bags. The evaluatin criteria for calculating this out-of-box error is specified by the user (see class CEvaluation). More...|