CatBoost

Yandex's gradient boosting that handles categorical features natively without encoding.

When to use:

• Have categorical features
• Don't want to manually encode categories
• Need robust performance with minimal tuning
• Noisy data

Strengths: Handles categories natively, less overfitting, good default parameters, robust to noise Weaknesses: Slower than LightGBM, larger models

Model Parameters

Iterations (default: 100) Number of boosting iterations.

Depth (default: 6) Tree depth.

Learning Rate (default: 0.03) Lower default than other boosting models.

L2 Leaf Reg (default: 3.0) L2 regularization coefficient.

Border Count (default: 254) Number of splits for numerical features.

Random State (default: 42) Seed for reproducibility.