What Decision Tree Meaning, Applications & Example
A tree-like model that breaks down a dataset into subsets.
What is a Decision Tree?
A Decision Tree is a supervised learning algorithm used for both classification and regression tasks. It models decisions and their possible consequences in a tree-like structure, where each node represents a decision point based on an attribute, and each branch represents an outcome.
Types of Decision Trees
- Classification Trees: Used to classify data into categories, where the leaves represent class labels.
- Regression Trees: Used for continuous data, where the leaves represent numeric values or averages.
- CART (Classification and Regression Tree): A popular algorithm that can construct both classification and regression trees.
Applications of Decision Trees
- Customer Segmentation: Categorizes customers based on attributes like purchase history, demographics, and behavior.
- Medical Diagnosis: Assists in diagnosing diseases by analyzing patient symptoms and medical history.
- Credit Scoring: Evaluates loan eligibility by analyzing factors such as income, credit history, and debt.
Example of a Decision Tree
An example of a Decision Tree is in loan approval systems, where it helps banks determine if an applicant is eligible for a loan based on factors such as credit score, income, and loan amount requested.