boosting

Boosting is a machine learning technique aimed at improving the accuracy of models. It works by combining the predictions of several weak models, which perform slightly better than random chance. Each model is trained in sequence, focusing more on the data points that previous models misclassified. This way, boosting adjusts for errors and enhances overall performance. The final prediction is made by aggregating the outputs of all models, often resulting in a much stronger prediction than any individual model could achieve on its own. It's widely used in tasks like classification and regression.