What are Gradient Boosting Machines, and how do they differ from other ensemble methods like Random Forests? Explain the core principles behind the gradient boosting technique, including how it combines weak learners sequentially to create a strong predictive model. Discuss key components such as the loss function, base learners (typically decision trees), and the concept of boosting through iterative model improvement. Additionally, highlight the advantages of Gradient Boosting Machines in terms of predictive accuracy and their ability to handle complex relationships in data. Could you also elaborate on scenarios or considerations where Gradient Boosting might be preferable or less suitable compared to other algorithms or ensemble techniques?

Gradient Boosting Machines (GBMs) are a type of ensemble learning method used for classification and regression tasks. They work by combining multiple weak learners (often decision trees) sequentially to create a strong predictive model. GBMs belong to...