Decision Tree | Synced

by Synced 2023-10-02 0

Standford U’s MAPTree: Redefining Decision Trees – Precision, Speed, and Efficiency Unleashed

In a new paper MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees, a Stanford University research team introduces MAPTree, an algorithm that confidently uncovers the maximum a posteriori tree within Bayesian Classification and Regression Trees (BCART) posterior, achieving strong performance with significantly leaner and faster trees.

by Synced 2022-07-26 1

AI Machine Learning & Data Science Research

Microsoft, DP Technology & Tsinghua U Enable Efficient Low-Precision Training of Gradient Boosting Decision Trees

In the new paper Quantized Training of Gradient Boosting Decision Trees, a team from Microsoft Research, DP Technology and Tsinghua University proposes a method for the low-precision training of gradient boosting decision trees via gradient quantization.