In this chapter, we shall use the gradient boosted trees and random forest implementation as pre-made estimators in TensorFlow from the Google TensorFlow team. Let us learn the details of their implementation in the upcoming sections.
TensorForest is a highly scalable implementation of random forests built by combining a variety of online HoeffdingTree algorithms with the extremely randomized approach.
Note
Google published the details of the TensorForest implementation in the following paper: TensorForest: Scalable Random Forests on TensorFlow by Thomas Colthurst, D. Sculley, Gibert Hendry, Zack Nado, presented at Machine Learning Systems Workshop at the Conference on Neural Information Processing Systems (NIPS) 2016. The paper is available at the following link: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtbHN5c25pcHMyMDE2fGd4OjFlNTRiOWU2OGM2YzA4MjE.
TensorForest estimators are used to implementing...