Learns a random forest* (an ensemble of decision trees) for regression. Each of the decision tree models is built with a different set of rows (records) and for each split within a tree a randomly chosen set of columns (describing attributes) is used. The row sets for each decision tree are created by bootstrapping and have the same size as the original input table. The attribute set for an individual split in a decision tree is determined by randomly selecting sqrt(m) attributes from the available attributes where m is the total number of learning columns. 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.

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 *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.