Boosting Algorithm

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Boosting Algorithm: A High-Level Overview

Boosting is an ensemble learning technique that sequentially trains multiple weak models (typically decision trees), where each model learns from the mistakes of the previous ones. Unlike bagging, where models are trained independently, boosting builds models in sequence, giving more importance to misclassified instances in each iteration.

Key Concepts of Boosting:

1. Weak Learners: Boosting typically uses small decision trees (stumps) as weak learners.
2. Sequential Learning: Each new model corrects the errors of the previous model.
3. Error Reduction: Some boosting algorithms (e.g., AdaBoost) adjust weights for misclassified samples, while others (e.g., Gradient Boosting) use gradient descent to minimize errors.
4. Final Prediction: The outputs of all weak models are combined (weighted sum or majority vote) to make a strong final prediction.

Popular Boosting Algorithms:

1. AdaBoost (Adaptive Boosting): Adjusts sample weights after each iteration, focusing more on misclassified points.
2. Gradient Boosting: Uses gradient descent to minimize errors at each step.
3. XGBoost (Extreme Gradient Boosting): An optimized version of gradient boosting with regularization and parallel processing for better performance.
4. LightGBM & CatBoost: Advanced boosting methods optimized for speed and handling categorical data efficiently.