Decision Tree
A supervised learning algorithm that makes predictions by learning a series of if-then-else decision rules from features, visualizable as a tree structure.
How It Works
The algorithm recursively splits data based on feature values that best separate classes (using criteria like Gini impurity or information gain). Each leaf node represents a prediction. The path from root to leaf forms an interpretable decision rule.
Strengths and Limitations
Highly interpretable, handles mixed data types, requires minimal preprocessing. But individual trees tend to overfit. Ensemble methods (Random Forest, Gradient Boosting) combine many trees for much better performance.