Boosting
Boosting is an ensemble machine learning technique that combines multiple weak learners (typically decision trees) into a strong learner by sequentially training models, where each new model focuses on correcting the errors of the previous ones. It works by assigning higher weights to misclassified instances in each iteration, improving overall predictive accuracy. This approach is widely used for classification and regression tasks in data science and artificial intelligence.
Developers should learn boosting when working on predictive modeling projects that require high accuracy, such as fraud detection, customer churn prediction, or medical diagnosis, as it often outperforms single models. It is particularly useful for handling complex, non-linear relationships in data and reducing bias and variance, making it a go-to method in competitions like Kaggle and real-world applications where performance is critical.