Module 1: Random Forest Theory
From the Mastering Random Forests & Ensemble Learning curriculum · Updated Jan 11, 2026
How Random Forests Work
Random Forest is an ensemble learning method that operates by constructing a multitude of Decision Trees at training time. It corrects for the habit of decision trees overfitting to their training set.
Key Concepts:
- Bagging (Bootstrap Aggregating): Random forests allow each tree to pick only a random sample of the data. This reduces variance.
- Feature Randomness: Each tree can only pick from a random subset of features. This forces trees to be more diverse.
Note: A single decision tree has high variance (it overfits). A random forest has lower variance but slightly higher bias.
Get the full Mastering Random Forests & Ensemble Learning curriculum
Clone the complete plan to your dashboard for unlimited AI-generated notes, practice quizzes, and a personalised revision schedule.
Create Free Account