Decision trees are a family of supervised machine learning models used for classification and regression that represent decisions as a hierarchical, tree-like structure. Beginning at a root node, the data are recursively partitioned: each internal node tests an attribute, each branch corresponds to an outcome of tha…
Decision trees are a family of supervised machine learning models used for classification and regression that represent decisions as a hierarchical, tree-like structure. Beginning at a root node, the data are recursively partitioned: each internal node tests an attribute, each branch corresponds to an outcome of that test, and each leaf assigns a class label or a predicted value. The tree is constructed by repeatedly selecting the attribute and split that best separate the data according to a chosen criterion, such as information gain based on entropy or impurity measures, with classic algorithms including ID3 and its successor C4.5 formalizing this top-down, greedy induction. A central appeal of decision trees is interpretability: the learned rules can be read and visualized directly, making the basis of predictions transparent, and they handle both numerical and categorical features with limited preprocessing. To control overfitting, trees are typically pruned or constrained in depth and leaf size. Individual trees can be unstable and are therefore often combined into ensembles, where many trees are aggregated to improve predictive accuracy and robustness while sacrificing some transparency. Decision trees and their ensembles are applied across diverse domains, from prioritizing and classifying tasks to predicting clinical outcomes such as disease risk from structured datasets. As intuitive, versatile predictors, they remain foundational in data mining and analytics.
5 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 5 articles above have been cited 39 times 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 Trees, linking to each citing work.