Beginner · Fundamentals
Decision tree learning
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A machine learning method that uses a tree-like structure to make predictions by recursively splitting data based on feature values.
Technical Definition
A machine learning method that uses a tree-like structure to make predictions by recursively splitting data based on feature values.
How it works
Decision tree learning is a supervised learning approach that builds a predictive model in the form of a tree. Each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or decision. It's widely used in data mining and statistics.
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.