About Random Forest

Random Forest is a classification algorithm that builds an ensemble (also called forest) of trees.

The algorithm builds a number of Decision Tree models and predicts using the ensemble. An individual decision tree is built by choosing a random sample from the training data set as the input. At each node of the tree, only a random sample of predictors is chosen for computing the split point. This introduces variation in the data used by the different trees in the forest. The parameters RFOR_SAMPLING_RATIO and RFOR_MTRY are used to specify the sample size and number of predictors chosen at each node. Users can use ODMS_RANDOM_SEED to set the random seed value before running the algorithm.