Definition:
A supervised learning algorithm used for classification and regression. It splits data into branches based on feature values to make decisions.
Key Points:
Tree structure: Root → Branch → Leaf
Root: Main feature
Leaf: Predicted outcome
Splitting is based on criteria like Gini Index, Entropy, or Variance
Applications:
Loan approval
Customer segmentation
Medical diagnosis
Advantages:
Easy to understand and interpret
Handles both numerical and categorical data
Limitations:
Prone to overfitting
Can be unstable with small data changes
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