Decision Tree

Single tree that makes predictions using if-then-else rules.

When to use:

• Need interpretable model
• Want to visualize decisions
• Teaching/explaining ML
• Quick baseline

Strengths: Highly interpretable, visualizable, handles non-linear, no feature scaling needed Weaknesses: Overfits easily, unstable, less accurate than ensembles

Model Parameters

Max Depth Maximum tree depth. Lower = simpler, prevents overfitting.

Min Samples Split (default: 2) Minimum samples to create a split.

Min Samples Leaf (default: 1) Minimum samples in leaf nodes.

Splitter

• best: Choose best split (default)
• random: Random split (faster, more randomness)

Random State (default: 42) Seed for reproducibility.