Boosted trees

A boosted trees is a type of machine learning model that is used to make predictions. It works by creating many decision trees, which are like flowcharts that ask a series of questions to figure out the answer to a problem. The boosted trees combines the answers from all of the decision trees to make a final prediction. It’s called a “boosted” tree because it creates the decision trees in a way that helps the model make better predictions.

To create the decision trees, the boosted trees starts by making a simple decision tree and then adds more decision trees one at a time. Each time a new decision tree is added, it focuses on the mistakes that the previous decision trees made and tries to improve upon them. This process is repeated many times until the model is accurate enough. boosted trees are often used in situations where it is important to make very accurate predictions, such as in medical diagnosis or credit fraud detection.