Learns a random forest* (an ensemble of decision trees) for regression. Each of the regression tree models is learned on a different set of rows (records) and/or a different set of columns (describing attributes), whereby the latter can also be a bit-vector or byte-vector descriptor (e.g. molecular fingerprint). The output model describes an ensemble of regression tree models and is applied in the corresponding predictor node using a simply mean of the individual predictions.
For a more general description and suggested default parameters see the node description of the classification Random Forest Learner node.
This node provides a subset of the functionality of the Tree Ensemble Learner (Regression) . If you need additional functionality, please check out the Tree Ensemble Learner (Regression)
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.