-
Introduction to Gradient Boosting
A beginner-friendly introduction to gradient boosting, connecting empirical risk minimization, adaptive basis functions, and the challenges of non-differentiable models like decision trees.
-
Boosting and AdaBoost
This blog post provides an in-depth overview of boosting techniques, focusing on AdaBoost, explaining its key concepts, algorithm steps, and real-world applications in classification tasks.
-
Random Forests
Explore how Random Forests enhance Bagging by introducing randomness at each tree split, reducing correlation, and increasing diversity to build more accurate and stable prediction models.
-
Bagging - Bootstrap Aggregation
Bagging (Bootstrap Aggregating) combines multiple high-variance models trained on different bootstrap samples to create a more stable, accurate, and lower-variance ensemble predictor.
-
Introduction to Ensemble Methods
A beginner's guide to ensemble methods in machine learning, explaining how averaging and bootstrapping reduce variance and improve model performance.