Parses a given list of options.
Valid options are:
-P
Size of each bag, as a percentage of the
training set size. (default 100)
-O
Calculate the out of bag error.
-store-out-of-bag-predictions
Whether to store out of bag predictions in internal evaluation object.
-output-out-of-bag-complexity-statistics
Whether to output complexity-based statistics when out-of-bag evaluation is performed.
-print
Print the individual classifiers in the output
-attribute-importance
Compute and output attribute importance (mean impurity decrease method)
-I <num>
Number of iterations.
(current value 100)
-num-slots <num>
Number of execution slots.
(default 1 - i.e. no parallelism)
(use 0 to auto-detect number of cores)
-K <number of attributes>
Number of attributes to randomly investigate. (default 0)
(<1 = int(log_2(#predictors)+1)).
-M <minimum number of instances>
Set minimum number of instances per leaf.
(default 1)
-V <minimum variance for split>
Set minimum numeric class variance proportion
of train variance for split (default 1e-3).
-S <num>
Seed for random number generator.
(default 1)
-depth <num>
The maximum depth of the tree, 0 for unlimited.
(default 0)
-N <num>
Number of folds for backfitting (default 0, no backfitting).
-U
Allow unclassified instances.
-B
Break ties randomly when several attributes look equally good.
-output-debug-info
If set, classifier is run in debug mode and
may output additional info to the console
-do-not-check-capabilities
If set, classifier capabilities are not checked before classifier is built
(use with caution).
-num-decimal-places
The number of decimal places for the output of numbers in the model (default 2).
-batch-size
The desired batch size for batch prediction (default 100).