Gradient Boosting Machines

Gradient Boosting Machines (GBM) are a type of machine learning algorithm that builds models in a stage-wise fashion. They work by combining the predictions of several base learners, typically decision trees, to create a strong predictive model. The key characteristic of GBM is its ability to optimize loss functions through gradient descent, which allows it to improve accuracy iteratively. Common use cases include regression tasks, classification problems, and ranking challenges in various fields such as finance, healthcare, and marketing.