Decision Tree Nodes - Ensembles

These settings determine the behavior of ensembling that occurs when boosting, bagging, or very large datasets are requested in Objectives. Options that do not apply to the selected objective are ignored.

Bagging and Very Large Datasets. When scoring an ensemble, this is the rule used to combine the predicted values from the base models to compute the ensemble score value.

• Default combining rule for categorical targets. Ensemble predicted values for categorical targets can be combined using voting, highest probability, or highest mean probability. Voting selects the category that has the highest probability most often across the base models. Highest probability selects the category that achieves the single highest probability across all base models. Highest mean probability selects the category with the highest value when the category probabilities are averaged across base models.
• Default combining rule for continuous targets. Ensemble predicted values for continuous targets can be combined using the mean or median of the predicted values from the base models.

Note that when the objective is to enhance model accuracy, the combining rule selections are ignored. Boosting always uses a weighted majority vote to score categorical targets and a weighted median to score continuous targets.

Boosting and Bagging. Specify the number of base models to build when the objective is to enhance model accuracy or stability; for bagging, this is the number of bootstrap samples. It should be a positive integer.