Category: Ensemble Learning

Gradient boosting is one of popular boosting technique, especially with decision trees, for regression and classification problems. Like other boosting methods, gradient boosting adds predictors sequentially to an ensemble that … Read More

Random forests are an ensemble method (generally, bagging) constructing multiple decision trees on random samples with replacement of the training set. It is a supervised learning algorithm and can be … Read More

Bagging is short for bootstrap aggregating. This is one of the most popular ensemble learning approaches. The main idea of bagging is that it samples subsets from the training set … Read More