Gradient Boosting Machines
Gradient Boosting Machines (GBM) are a powerful ensemble machine learning technique that builds predictive models by combining multiple weak learners, typically decision trees, in a sequential manner. It works by iteratively fitting new models to the residuals (errors) of previous models, using gradient descent to minimize a loss function, which improves accuracy and reduces bias. This approach is widely used for regression and classification tasks due to its high performance and flexibility in handling various data types.
Developers should learn GBM when working on structured data problems requiring high predictive accuracy, such as in finance for credit scoring, in e-commerce for recommendation systems, or in healthcare for disease prediction. It is particularly useful when dealing with non-linear relationships and complex interactions in data, as it often outperforms simpler models like linear regression or single decision trees. However, it requires careful tuning of hyperparameters to avoid overfitting and can be computationally intensive for large datasets.