Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods

TL;DR

This paper evaluates a nonlinear decision tree approach for binary classification, comparing it with existing methods across various datasets to analyze accuracy, interpretability, and parameter effects.

Contribution

It provides a comprehensive evaluation of a recent nonlinear decision tree method against other classifiers, highlighting its strengths and limitations.

Findings

Nonlinear decision trees can achieve competitive accuracy.

Parameter settings significantly affect classifier performance.

Trade-offs exist between complexity and interpretability.

Abstract

Classification of datasets into two or more distinct classes is an important machine learning task. Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation for users to have a deeper understanding of reasons for the split of data into two classes. In this paper, we highlight and evaluate a recently proposed nonlinear decision tree approach with a number of commonly used classification methods on a number of datasets involving a few to a large number of features. The study reveals key issues such as effect of classification on the method's parameter values, complexity of the classifier versus achieved accuracy, and interpretability of resulting classifiers.