What iterative ensemble learning method builds multiple decision trees to reduce errors?

The iterative ensemble learning method that builds multiple decision trees to reduce errors is gradient boosting. This approach focuses on building models in a sequential manner, where each new model attempts to correct errors made by the previous models. In gradient boosting, decision trees are trained incrementally, with each tree fitting to the residual errors of the combined predictions from the previous trees. This method systematically reduces bias and variance, leading to improved predictive performance.

Gradient boosting employs a technique where the output of the weak learners (the individual decision trees) is combined in a way that optimizes a particular loss function, thus minimizing the error gradually. This stands in contrast to other ensemble methods like bagging and random forests, which reduce model variance by averaging predictions from multiple trees built independently. AdaBoost, while also an ensemble method, weights instances to focus on misclassified data points, which differs from the gradient boosting approach where new trees are explicitly created to minimize overall error in a gradient descent manner.

The effectiveness of gradient boosting is often evident in various applications and competitions, where it is favored for its ability to achieve high accuracy through its iterative refinement process. This setup makes it particularly powerful for complex datasets and tasks.