Two-Class Boosted Decision Tree

Two-Class Boosted Decision Tree module creates a machine learning model that is based on the boosted decision trees algorithm. A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Predictions are based on the entire ensemble of trees together that makes the prediction. Step 1 Add the Boosted Decision Tree module to the experiment. Step 2 Specify how you want the model to be trained, by setting the Create trainer mode option.