| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built
(use with caution). | |
| A | If set, average estimate is used rather than one estimate from pooled predictions. | |
| E | The number of threads to use, which should be >= size of thread pool.
(default 1) | default: 1 |
| F | Number of folds for cross-validation.
(default 10) | default: 10 |
| H | Shrinkage parameter.
(default 1) | |
| I | Step size for the evaluation, if evaluation is time consuming.
(default 1) | default: 1 |
| L | The number of iterations to look ahead for to find a better optimum.
(default 50) | default: 50 |
| O | The size of the thread pool, for example, the number of cores in the CPU. (default 1) | |
| P | The size of the thread pool, for example, the number of cores in the CPU.
(default 1) | default: 1 |
| Q | Use resampling instead of reweighting for boosting. | |
| R | Number of runs for cross-validation.
(default 1) | default: 1 |
| S | Random number seed.
(default 1) | default: 1 |
| W | Full name of base classifier.
(default: weka.classifiers.meta.LogitBoost) | default: weka.classifiers.meta.LogitBoost |
| Z | Z max threshold for responses.
(default 3) | |
| batch-size | The desired batch size for batch prediction (default 100). | |
| class-value-index | Class value index to optimise. Ignored for all but information-retrieval
type metrics (such as roc area). If unspecified (or a negative value is supplied),
and an information-retrieval metric is specified, then the class-weighted average
metric used. (default -1) | |
| metric | Evaluation metric to optimise (default rmse). Available metrics:
correct,incorrect,kappa,total cost,average cost,kb relative,kb information,
correlation,complexity 0,complexity scheme,complexity improvement,
mae,rmse,rae,rrse,coverage,region size,tp rate,fp rate,precision,recall,
f-measure,mcc,roc area,prc area | default: RMSE |
| num-decimal-places | The number of decimal places for the output of numbers in the model (default 2). | |
| order | Whether to preserve order when a percentage split evaluation is performed. | |
| output-debug-info | If set, classifier is run in debug mode and
may output additional info to the console | |
| percentage | The percentage of data to be used for training (if 0, k-fold cross-validation is used)
(default 0) | default: 0.0 |
| use-estimated-priors | Use estimated priors rather than uniform ones. | |