Types of Boosting in Machine Learning

Boosting is a powerful ensemble learning technique in machine learning that combines multiple weak models to create a strong predictive model. It improves accuracy by sequentially training models and correcting previous errors. Boosting is widely used in various applications, including classification and regression tasks.

The different types of Boosting are as follows:

• Adaptative boosting
• Gradient Boosting
• XGBoost

Adaptive Boosting (AdaBoost)

AdaBoost works by combining multiple weak learners, typically decision trees with a single split (stumps). It assigns higher weights to misclassified samples, making the next model focus more on these difficult cases. This process continues iteratively, leading to a strong classifier.

Gradient Boosting

Gradient Boosting builds models sequentially, with each new model correcting the residual errors of the previous one. It uses gradient descent to optimize the loss function, making it highly effective in reducing errors. This method is commonly used in decision tree-based models.

XGBoost

XGBoost (Extreme Gradient Boosting) is an optimized version of Gradient Boosting. It is faster, more efficient, and includes advanced features like regularization, handling missing values, and parallel processing. XGBoost is widely used in machine learning competitions and real-world applications.