Learns an ensemble of regression trees (such as random forest* variants). Typically, each tree is built with a different set of rows (records) and/or columns (attributes). See the options for Data Sampling and Attribute Sampling for more details. The attributes can also be provided as bit (fingerprint), byte, or double vector. The output model describes an ensemble of regression tree models and is applied in the corresponding predictor node using a simple mean of the individual predictions.
In a regression tree the predicted value for a leaf node is the mean target value of the records within the leaf. Hence the predictions are best (with respect to the training data) if the variance of target values within a leaf is minimal. This is achieved by splits that minimize the sum of squared errors in their respective children.
For a more general description and suggested default parameters see the node description of the classification Tree Ensemble Learner .
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.