Boosting Algorithms

Boosting algorithms are techniques in machine learning that improve the accuracy of models by combining multiple weak learners—simple models that perform just better than random guessing—into a stronger, more accurate one. Each learner focuses on correcting errors made by previous ones, effectively "learning from mistakes." By iteratively emphasizing difficult cases, boosting creates a robust model capable of making precise predictions. Common boosting methods include AdaBoost and Gradient Boosting, widely used for tasks like classification and regression due to their high performance and ability to handle complex data patterns.