juniorDecision Trees
What is a Decision Tree in Machine Learning?
Updated May 16, 2026
Short answer
A Decision Tree is a supervised learning model that splits data into branches based on feature conditions to make predictions.
Deep explanation
A Decision Tree works by recursively splitting the dataset into subsets based on feature values. Each internal node represents a decision based on an attribute, each branch represents an outcome, and each leaf node represents a prediction. The goal is to create homogeneous subsets using metrics like Gini impurity or entropy.
Real-world example
Used in loan approval systems to classify applicants as eligible or not based on income, credit score, and history.
Common mistakes
- Assuming decision trees always generalize well without pruning.
Follow-up questions
- What are internal and leaf nodes?
- Is Decision Tree supervised or unsupervised?