Learns an ensemble of decision trees (such as random forest* variants). Each of the decision tree models is learned on a different set of rows (records) and/or a different set of columns (describing attributes), whereby the latter can also be a bit/byte-vector descriptor (e.g. molecular fingerprint). The output model describes an ensemble of decision tree models and is applied in the corresponding predictor node using a simply majority vote.
The following configuration settings learn a model that is similar to the random forest™ classifier described by Leo Breiman and Adele Cutler:
The decision tree construction takes place in main memory (all data and all models are kept in memory).
Select the attributes to use learn the model. Two variants are possible.
Fingerprint attribute uses the different bit/byte positions in the selected bit/byte vector as learning attributes (for instance a bit vector of length 1024 is expanded to 1024 binary attributes or 1024 long byte vector is expanded to the corresponding number of numeric attributes). All 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.
Use same set of attributes for each tree describes that the attributes are sampled once for each tree and this sample is then used to construct the tree.
Use different set of attributes for each tree node samples a different set of candidate attributes in each of the tree nodes from which the optimal one is chosen to perform the split.
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