METHOD AND APPARATUS FOR TRAINING MODEL BASED ON RANDOM FOREST

摘要

Disclosed are a method and an apparatus for training a model based on a random forest. The method comprises: dividing worker nodes into one or more groups (101); worker nodes of each group randomly sampling preset sample data so as to obtain target sample data (102); worker nodes of each group using the target sample data to train one or more decision tree objects (103). The method of the present invention has the advantage that it is not necessary to completely scan all sample data at one time, reducing greatly the volume of data to be read, thereby reducing time needed for iterative updating of a model and improving training efficiency.