Intermediate · Research
Decision tree
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A decision tree is a supervised learning model that uses a tree-like structure of decisions and their possible consequences.
Technical Definition
A decision tree is a supervised learning model that uses a tree-like structure of decisions and their possible consequences.
How it works
A decision tree is a supervised machine learning algorithm that works by recursively partitioning the data based on the values of features. It creates a tree-like structure where internal nodes represent tests on features, branches represent the outcomes of these tests, and leaf nodes represent the final predictions (class labels or values). They are interpretable but can be prone to overfitting.
Related Concepts
- Classification — A supervised learning task where the model assigns inputs to discrete categories.
- Regression — A supervised learning task that predicts a continuous numeric value.
- Supervised Learning — Learning from input–output pairs where each training example carries a correct label.