Boosting is a powerful ensemble learning technique used in machine learning to improve the accuracy of predictive models. It involves combining multiple weak learners, which are models that perform slightly better than random guessing, to create a strong learner. The process is iterative, where each new model is trained to correct the errors made by the previous models. This approach helps in reducing bias and variance, leading to better overall performance.

The concept of boosting can be traced back to the work of Jerome H. Friedman, who introduced gradient boosting in 2001. This method has since evolved into various forms, including AdaBoost and Gradient Boosting, each with its unique characteristics and applications. The general idea is to focus on the instances that previous models misclassified, thereby improving the model’s predictive capabilities over time ¹²³⁴.

Boosting is widely used in various applications, including classification and regression tasks, and is known for its effectiveness in handling complex datasets.