Global Evaluation for Decision Tree Learning

TL;DR

This paper introduces a global evaluation method for decision tree learning that considers the overall tree accuracy rather than local leaf-based metrics, leading to potential improvements in tree quality.

Contribution

It extends the ID3 algorithm by incorporating global distance measures, providing a novel approach to decision tree training and evaluation.

Findings

Global evaluation improves decision tree quality in some scenarios

The approach reveals strengths and limitations of global assessments

Potential applications in scenarios requiring holistic tree evaluation

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.