conTree-package {conTree}

Contrast and Boosted Trees

Description

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

Author(s)

Original code (C) by Jerome H. Friedman, minor modifications, formatting, and packaging by Balasubramanian Narasimhan

References

Jerome Friedman (2019). Contrast Trees and Distribution Boosting https://arxiv.org/abs/1912.03785