Artificial Intelligence & Machine Learning
Decision Tree
Definition
A decision tree is a supervised learning algorithm that is used for both classification and regression tasks. It is a flowchart-like structure in which 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 a continuous value.
Why It Matters
Decision trees are easy to understand and interpret. Their visual, flowchart-like nature makes them a great tool for explaining the decision-making process of a model.
Contextual Example
A decision tree to approve a loan might first ask "Is income > $50,000?". If yes, it might then ask "Is credit score > 650?". By following the branches, you arrive at a final decision: "Approve" or "Deny."
Common Misunderstandings
- A single decision tree can be prone to overfitting. Ensembles of decision trees, like Random Forests and Gradient Boosted Trees, are much more powerful.
- They are a "white box" model because their logic is easily visible.