What is Gradient Boosting Machines?

Gradient Boosting Machines (GBM) are a type of machine learning algorithm that combines the predictions from multiple weaker models, typically decision trees, to create a stronger, more accurate prediction. The 'gradient' refers to the use of gradient descent to minimize the loss function in the boosting process. GBMs are commonly used for regression and classification tasks, and are known for their high accuracy and ability to handle complex relationships in data. They are popular in various applications such as fraud detection, risk assessment, and predicting customer behavior.