| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built
(use with caution). | |
| B | Break ties randomly when several attributes look equally good. | |
| I | Number of iterations.
(current value 100) | default: 100 |
| K | Number of attributes to randomly investigate. (default 0)
(<1 = int(log_2(#predictors)+1)). | default: 0 |
| M | Set minimum number of instances per leaf.
(default 1) | default: 1.0 |
| N | Number of folds for backfitting (default 0, no backfitting). | |
| O | Calculate the out of bag error. | |
| P | Size of each bag, as a percentage of the
training set size. (default 100) | default: 100 |
| S | Seed for random number generator.
(default 1) | default: 1 |
| U | Allow unclassified instances. | |
| V | Set minimum numeric class variance proportion
of train variance for split (default 1e-3). | default: 0.001 |
| attribute-importance | Compute and output attribute importance (mean impurity decrease method) | |
| batch-size | The desired batch size for batch prediction (default 100). | |
| depth | The maximum depth of the tree, 0 for unlimited.
(default 0) | |
| num-decimal-places | The number of decimal places for the output of numbers in the model (default 2). | |
| num-slots | Number of execution slots.
(default 1 - i.e. no parallelism)
(use 0 to auto-detect number of cores) | default: 1 |
| output-debug-info | If set, classifier is run in debug mode and
may output additional info to the console | |
| 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 | |
| store-out-of-bag-predictions | Whether to store out of bag predictions in internal evaluation object. | |