Batched Lazy Decision Trees

TL;DR

This paper presents a batched lazy decision tree algorithm that improves prediction efficiency by reducing unnecessary node visits, outperforming traditional methods in speed and memory use without sacrificing accuracy.

Contribution

The paper introduces a novel batched lazy algorithm for decision trees that enhances prediction efficiency and resource usage over existing methods.

Findings

Outperforms conventional decision trees in computation time.

Uses less memory than traditional lazy decision trees.

Maintains accuracy comparable to existing algorithms.

Abstract

We introduce a batched lazy algorithm for supervised classification using decision trees. It avoids unnecessary visits to irrelevant nodes when it is used to make predictions with either eagerly or lazily trained decision trees. A set of experiments demonstrate that the proposed algorithm can outperform both the conventional and lazy decision tree algorithms in terms of computation time as well as memory consumption, without compromising accuracy.