| Issue | #Downvotes for this reason | By |
|---|
| K | weka.NormalizedPolyKernel(7) | The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel) |
| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built (use with caution). | |
| C | The complexity constant C. (default 1) | default: 1.0 |
| E | The Exponent to use. (default: 1.0) | |
| K | The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel) | default: weka.classifiers.functions.supportVector.NormalizedPolyKernel |
| L | The tolerance parameter. (default 1.0e-3) | default: 0.001 |
| M | Fit calibration models to SVM outputs. | |
| N | Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize) | default: 0 |
| P | The epsilon for round-off error. (default 1.0e-12) | default: 1.0E-12 |
| R | Set the ridge in the log-likelihood. | |
| V | The number of folds for the internal cross-validation. (default -1, use training data) | default: -1 |
| W | The random number seed. (default 1) | default: 1 |
| batch-size | The desired batch size for batch prediction (default 100). | |
| calibrator | Full name of calibration model, followed by options. (default: "weka.classifiers.functions.Logistic") | default: true |
| no-checks | Turns off all checks - use with caution! Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. (default: checks on) | |
| 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 |