Random Forest Learner (Regression)

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.

Input ports

1. Type: Table
   Input Data
   The data to learn from. They must contain at least one numeric target column and either a fingerprint (bit-vector/byte-vector) column or another numeric or nominal column.

Output ports

1. Type: Table
   Out-of-bag Predictions
   The input data with the out-of-bag predictions, i.e. for each input row the mean and variance of outputs of all models that did not use the row for training. The appended columns are equivalent to the columns appended by the corresponding predictor node. There is one additional column model count , which contains the number of models used for the voting (number of models not using the row throughout the learning.) The out-of-bag predictions can be used to get an estimate of the generalization ability of the random forest by feeding them into the Numeric Scorer node.
2. Type: Table
   Attribute Statistics
   A statistics table on the attributes used in the different tree learners. Each row represents one training attribute with these statistics: #splits (level x) as the number of models, which use the attribute as split on level x (with level 0 as root split); #candidates (level x) is the number of times an attribute was in the attribute sample for level x (in a random forest setup these samples differ from node to node). If no attribute sampling is used #candidates is the number of models. Note, these numbers are uncorrected, i.e. if an attribute is selected on level 0 but is also in the candidate set of level 1 (but is not split on level 1 because it has been split one level up), the #candidate number will still count the attribute as candidate.
3. Type: Tree Ensembles
   Random Forest Model
   The trained model.