Decision Tree Classification in Machine Learning (2026)

Decision tree classification is a supervised learning method that predicts a class label by following a sequence of feature-based decisions from the root of a tree to a leaf. Each internal node tests a feature, each branch represents an outcome of that test, and each leaf stores the final class prediction.

Entropy measures how mixed the class labels are inside a node: it is low when one class dominates and high when classes are evenly balanced. Information gain compares the entropy before and after a split. A good split produces child nodes with lower entropy, so the information gain is higher, and the algorithm prefers that split during training.

Decision trees are a good choice when you need an interpretable model for tabular data, want to understand which features drive predictions, or need a solid baseline. They can struggle with very high-dimensional sparse data or highly complex patterns, where models like ensembles, neural networks, or linear methods may perform better.