Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods

2 February 2022

Papers citing "Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods"

5 / 5 papers shown

Title
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds Yan Shuo Tan Abhineet Agarwal Bin Yu 59 11 0 18 Oct 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 218 673 0 20 Mar 2021
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods Joseph D. Romano Trang T. Le William La Cava John T. Gregg Daniel J. Goldberg Natasha L. Ray Praneel Chakraborty Daniel Himmelstein Weixuan Fu J. Moore GP 43 74 0 30 Nov 2020
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 72 743 0 05 Nov 2015
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 272 2,788 0 18 Aug 2015