Random Forests Explained: How This Classic Machine Learning Algorithm Works

This video explains the landmark 2001 paper “Random Forests” by Leo Breiman, one of the most influential machine learning algorithms used for classification and regression.

Random Forests is an ensemble learning method that combines multiple decision trees to improve predictive accuracy, robustness, and generalization performance.

🔍 In this video, you will learn:
What Random Forests is and why it was a breakthrough in machine learning
How combining many decision trees reduces overfitting
Why low correlation between trees improves model performance
The role of random feature selection at each split
How Random Forests outperform methods like AdaBoost in noisy data
What out-of-bag error estimation is and why it matters
How variable importance is calculated in Random Forests

📊 Key contributions of the article:
Introduction of a scalable and robust ensemble method
Internal error estimation without a separate validation set
Strong performance on large, complex, and mixed-type datasets
A theoretical foundation for ensemble learning

📚 Reference:
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
https://doi.org/10.1023/A:1010933404324

This video is ideal for:
Data scientists and machine learning practitioners
Students learning predictive modeling
Researchers working with complex datasets
Anyone seeking a clear explanation of a foundational ML algorithm