Random Forest. More...
#include <shark/Algorithms/Trainers/RFTrainer.h>
Random Forest.
Random Forest is an ensemble learner, that builds multiple binary decision trees. The trees are built using a variant of the CART methodology
Typically 100+ trees are built, and classification/regression is done by combining the results generated by each tree. Typically the a majority vote is used in the classification case, and the mean is used in the regression case
Each tree is built based on a random subset of the total dataset. Furthermore at each split, only a random subset of the attributes are investigated for the best split
The node impurity is measured by the Gini criteria in the classification case, and the total sum of squared errors in the regression case
After growing a maximum sized tree, the tree is added to the ensemble without pruning.
For detailed information about Random Forest, see Random Forest by L. Breiman et al. 2001.
Definition at line 72 of file RFTrainer.h.