| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built
(use with caution). | |
| -doNotMakeSplitPointActualValue | Do not make split point actual value. | |
| A | Laplace smoothing for predicted probabilities. | |
| B | Use binary splits only. | |
| C | Set confidence threshold for pruning.
(default 0.25) | |
| D | Output debugging info. | |
| E | Full class name of attribute evaluator, followed
by its options.
eg: "weka.attributeSelection.CfsSubsetEval -L"
(default weka.attributeSelection.CfsSubsetEval) | default: weka.attributeSelection.CfsSubsetEval -P 1 -E 1 |
| J | Do not use MDL correction for info gain on numeric attributes. | |
| L | Do not clean up after the tree has been built. | |
| M | Set minimum number of instances per leaf.
(default 2) | |
| N | Set number of folds for reduced error
pruning. One fold is used as pruning set.
(default 3) | |
| O | Do not collapse tree. | |
| P | The size of the thread pool, for example, the number of cores in the CPU. (default 1) | |
| Q | Seed for random data shuffling (default 1). | |
| R | Use reduced error pruning. | |
| S | Full class name of search method, followed
by its options.
eg: "weka.attributeSelection.BestFirst -D 1"
(default weka.attributeSelection.BestFirst) | default: weka.attributeSelection.BestFirst -D 1 -N 5 |
| U | Use unpruned tree. | |
| W | Full name of base classifier.
(default: weka.classifiers.trees.J48) | default: weka.classifiers.meta.AdaBoostM1 |
| Z | Precompute the full correlation matrix at the outset, rather than compute correlations lazily (as needed) during the search. Use this in conjuction with parallel processing in order to speed up a backward search. | |
| num-decimal-places | The number of decimal places for the output of numbers in the model (default 2). | |
| output-debug-info | If set, classifier is run in debug mode and
may output additional info to the console | |