Decision Tree

A decision support tool that uses a tree-like model of decisions and their possible consequences.

How it works

A decision tree is trained by recursively splitting the training data on the feature that provides the greatest information gain at each node — measured by metrics like Gini impurity or entropy reduction. Each internal node represents a test on a feature (e.g., 'is the temperature above 30°C?'), each branch represents an outcome of that test, and each leaf node contains a prediction. The tree is grown until leaves are pure or a stopping criterion (max depth, minimum samples) is reached.

Why it matters

Decision trees are the most interpretable of all machine learning models — you can literally follow the path a prediction took, making them invaluable in regulated industries like finance and healthcare where explainability is required. They also form the foundation for powerful ensemble methods: Random Forests and Gradient Boosted Trees (XGBoost, LightGBM) that consistently win structured data competitions and power real-world prediction systems.