What is gradient boosted trees?

Gradient boosted trees are a machine learning technique used for regression and classification tasks. They work by iteratively building an ensemble of decision trees, where each tree attempts to correct the errors made by the previous trees. This is done by fitting each new tree to the negative gradient of the loss function with respect to the current ensemble prediction. Gradient boosting is known for its high accuracy and ability to handle complex data, but it can be prone to overfitting if not properly regularized. It's commonly used in areas such as fraud detection, risk assessment, and search ranking.