Basic algorithm - 2023.2 English

Vitis Libraries

Release Date
2023.2 English

To ensure the diversity of each individual tree, Random Forest uses two methods:

Sample Bagging: Random forest allows each individual tree to randomly select instances from the dataset with replacement, resulting in different trees. This process is known as bagging.

Feauture Bagging: In decision tree, we consider every possible feature and pick the one that get most gains when splitting each tree node. In contrast, each tree in a random forest can pick only from a random subset of features. This forces even more variation amongst the trees in the model and ultimately results in lower correlation across trees and more diversification.


Current implementation provides without-replacement sample bagging to complete rf framework quickly. When compared with spark, we also choose the same bagging method. With-replacement bagging will come in later updates.