| -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 binary attributes for discretized attributes. | |
| E | Use better encoding of split point for MDL. | |
| F | Full class name of filter to use, followed
by filter options.
default: "weka.filters.supervised.attribute.Discretize -R first-last -precision 6" | default: weka.filters.supervised.attribute.Discretize -R first-last -precision 6 |
| J | Do not use MDL correction for info gain on numeric attributes. | |
| K | Use Kononenko's MDL criterion. | |
| 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. | |
| Q | Seed for random data shuffling (default 1). | |
| R | Use reduced error pruning. | |
| S | Random number seed.
(default 1) | default: 1 |
| U | Use unpruned tree. | |
| V | Invert matching sense of column indexes. | |
| W | Full name of base classifier.
(default: weka.classifiers.trees.J48) | default: weka.classifiers.trees.J48 |
| Y | Use bin numbers rather than ranges for discretized attributes. | |
| batch-size | The desired batch size for batch prediction (default 100). | |
| doNotCheckForModifiedClassAttribute | If set, classifier will not check whether the filter modifies the class (use with caution). | |
| 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 | |
| precision | Precision for bin boundary labels.
(default = 6 decimal places). | |
| spread-attribute-weight | When generating binary attributes, spread weight of old attribute across new attributes. Do not give each new attribute the old weight. | |