Boost

Boost is a machine learning technique that improves the accuracy of predictive models by combining multiple weak learners, usually decision trees, into a stronger overall model. Each weak learner makes predictions, and subsequent models focus more on data points that previous models misclassified. This iterative process continues, adjusting weights to emphasize difficult cases, resulting in a final model that balances these individual predictions more effectively. Boosting often leads to higher accuracy, especially with complex data, and is widely used in tasks like classification and regression.