Research Topic · Peer-Reviewed

Decision Tree Learning

Decision Tree Learning is a type of supervised machine learning algorithm that can be used to construct a prediction model from a data set. It works by mapping out possibilities and outcomes using a tree-like structure, in which each branch is based on a decision and each leaf is an outcome. By using what is known a…

Curated from this journal's research 📚 1 peer-reviewed article cited Cited 1× across the literature 🔖 ISSN 2643-2811 🗓 Reviewed June 2026

Overview

Decision Tree Learning is a type of supervised machine learning algorithm that can be used to construct a prediction model from a data set. It works by mapping out possibilities and outcomes using a tree-like structure, in which each branch is based on a decision and each leaf is an outcome. By using what is known as a splitting criteria, the decision tree is able to determine which class a data point belongs to. It can be used for both classification and regression problems, depending on the dataset and desired outcome. Decision Tree Learning is widely used in fields like data mining, statistics and predictive analytics, as it is an effective and efficient way to build models from data.

Research published in this journal

1 peer-reviewed article, ranked by relevance. Each links to its DOI.

How this research is being cited

The 1 article above has been cited 1 time in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Decision Tree Learning, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Model Based Research (ISSN 2643-2811).

Journal editorial board
Yoshiaki Kikuchi · Japan Yung-Yao Chen · Taiwan Yang Chen · United States

This page summarises published research for orientation; it is not medical or professional advice.