An Experimental Comparison of Old and New Decision Tree Algorithms
Machine Learning
2020-03-23 v2 Machine Learning
Abstract
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms. We compare their performance on a number of classification and regression datasets of various complexity, different size and dimensionality, across different performance factors: accuracy and tree size (in terms of the number of leaves or the depth of the tree). We find that TAO achieves higher accuracy in nearly all datasets, often by a large margin.
Cite
@article{arxiv.1911.03054,
title = {An Experimental Comparison of Old and New Decision Tree Algorithms},
author = {Arman Zharmagambetov and Suryabhan Singh Hada and Miguel Á. Carreira-Perpiñán and Magzhan Gabidolla},
journal= {arXiv preprint arXiv:1911.03054},
year = {2020}
}
Comments
12 pages, 0 figures