Learns a single regression tree. The procedures 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, missing values ignored, not necessarily binary trees, etc.
Select the attributes to use learn the model. Two variants are possible.
Fingerprint attribute uses the different bit/count positions in the selected bit/byte vector as learning attributes (for instance a bit/byte vector of length 1024 is expanded to 1024 binary/count attributes). All bit/byte vectors in the selected column must have the same length.
Column attributes are nominal and numeric columns used as descriptors. Numeric columns are split in a <= fashion; nominal columns are currently split by creating child nodes for each of the values.
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