| Issue | #Downvotes for this reason | By |
|---|
| E | weka.CfsSubsetEval(1) | Full class name of attribute evaluator, followed by its options. eg: "weka.attributeSelection.CfsSubsetEval -L" (default weka.attributeSelection.CfsSubsetEval) |
| S | weka.BestFirst(1) | Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst) |
| W | weka.LBR(1) | Full name of base classifier. (default: weka.classifiers.trees.J48) |
| A | Laplace smoothing for predicted probabilities. | |
| B | Use binary splits only. | |
| C | Set confidence threshold for pruning. (default 0.25) | |
| D | If set, classifier is run in debug mode and may output additional info to the console | |
| E | Full class name of attribute evaluator, followed by its options. eg: "weka.attributeSelection.CfsSubsetEval -L" (default weka.attributeSelection.CfsSubsetEval) | default: weka.attributeSelection.CfsSubsetEval |
| 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. | |
| 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 |
| U | Use unpruned tree. | |
| W | Full name of base classifier. (default: weka.classifiers.trees.J48) | default: weka.classifiers.lazy.LBR |