Global Evaluation for Decision Tree Learning
Machine Learning
2022-08-10 v1 Artificial Intelligence
Abstract
We transfer distances on clusterings to the building process of decision trees, and as a consequence extend the classical ID3 algorithm to perform modifications based on the global distance of the tree to the ground truth--instead of considering single leaves. Next, we evaluate this idea in comparison with the original version and discuss occurring problems, but also strengths of the global approach. On this basis, we finish by identifying other scenarios where global evaluations are worthwhile.
Cite
@article{arxiv.2208.04828,
title = {Global Evaluation for Decision Tree Learning},
author = {Fabian Spaeh and Sven Kosub},
journal= {arXiv preprint arXiv:2208.04828},
year = {2022}
}