Learns a single regression tree. The procedure follows the algorithm described by "Classification and Regression Trees" (Breiman et al, 1984), whereby the current implementation applies a couple of simplifications, e.g. no pruning, not necessarily binary trees, etc.
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.
The currently used missing value handling also differs from the one used by Breiman et al, 1984. In each split the algorithm tries to find the best direction for missing values by sending them in each direction and selecting the one that yields the best result (i.e. largest gain). The procedure is adapted from the well known XGBoost algorithm and is described here .
Select the attributes on which the model should be learned. You can choose from two modes.
Fingerprint attribute Uses a fingerprint/vector (bit, byte and double are possible) column to learn the model by treating each entry of the vector as separate attribute (e.g. a bit vector of length 1024 is expanded into 1024 binary attributes). The node requires all vectors to be of the same length.
Column attributes Uses ordinary columns in your table (e.g. String, Double, Integer, etc.) as attributes to learn the model on. The dialog allows to select the columns manually (by moving them to the right panel) or via a wildcard/regex selection (all columns whose names match the wildcard/regex are used for learning). In case of manual selection, the behavior for new columns (i.e. that are not available at the time you configure the node) can be specified as either Enforce exclusion (new columns are excluded and therefore not used for learning) or Enforce inclusion (new columns are included and therefore used for learning).
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