| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built
(use with caution). | |
| I | Number of iterations.
(current value 10) | default: 10 |
| L | Maximum tree depth (default -1, no maximum) | |
| M | Set minimum number of instances per leaf (default 2). | |
| N | Number of folds for reduced error pruning (default 3). | |
| O | Calculate the out of bag error. | |
| P | Size of each bag, as a percentage of the
training set size. (default 100) | default: 100 |
| R | Spread initial count over all class values (i.e. don't use 1 per value) | |
| S | Random number seed.
(default 1) | default: 1 |
| V | Set minimum numeric class variance proportion
of train variance for split (default 1e-3). | |
| W | Full name of base classifier.
(default: weka.classifiers.trees.REPTree) | default: weka.classifiers.functions.MultilayerPerceptron |
| batch-size | The desired batch size for batch prediction (default 100). | |
| 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 | |
| represent-copies-using-weights | Represent copies of instances using weights rather than explicitly. | |
| store-out-of-bag-predictions | Whether to store out of bag predictions in internal evaluation object. | |