Random Forest builds hundreds of decision trees and combines their predictions — dramatically outperforming any single tree.

How It Works

1. Randomly sample the training data (with replacement) — bagging
2. At each split, consider only a random subset of features
3. Build many trees, each slightly different
4. For classification: majority vote. For regression: average

Why It's Great

• Highly accurate out of the box
• Handles missing values well
• Provides feature importance scores
• Resistant to overfitting

When to Use

When you need a strong baseline fast. Random Forest is one of the most reliable all-purpose algorithms.

Reference:

scikit-learn ensemble methods

https://scikit-learn.org/stable/modules/ensemble.html