| -do-not-check-capabilities | If set, classifier capabilities are not checked before classifier is built
(use with caution). | |
| B | Generate probability estimates for classification | |
| C | Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
(default: 1) | default: 1.0 |
| D | Set degree in kernel function (default: 3) | default: 3 |
| E | Set tolerance of termination criterion (default: 0.001) | default: 0.001 |
| G | Set gamma in kernel function (default: 1/k) | default: 0.0 |
| H | Turns the shrinking heuristics off (default: on) | |
| J | Turn off nominal to binary conversion.
WARNING: use only if your data is all numeric! | |
| K | Set type of kernel function (default: 2)
0 = linear: u'*v
1 = polynomial: (gamma*u'*v + coef0)^degree
2 = radial basis function: exp(-gamma*|u-v|^2)
3 = sigmoid: tanh(gamma*u'*v + coef0) | default: 2 |
| M | Set cache memory size in MB (default: 40) | default: 40.0 |
| N | Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
(default: 0.5) | default: 0.5 |
| P | Set the epsilon in loss function of epsilon-SVR (default: 0.1) | default: 0.1 |
| R | Set coef0 in kernel function (default: 0) | default: 0.0 |
| S | Set type of SVM (default: 0)
0 = C-SVC
1 = nu-SVC
2 = one-class SVM
3 = epsilon-SVR
4 = nu-SVR | default: 0 |
| V | Turn off missing value replacement.
WARNING: use only if your data has no missing values. | |
| W | Set the parameters C of class i to weight[i]*C, for C-SVC.
E.g., for a 3-class problem, you could use "1 1 1" for equally
weighted classes.
(default: 1 for all classes) | |
| Z | Turns on normalization of input data (default: off) | |
| model | Specifies the filename to save the libsvm-internal model to.
Gets ignored if a directory is provided. | default: /home/azureuser |
| output-debug-info | If set, classifier is run in debug mode and
may output additional info to the console | |
| seed | Random seed
(default = 1) | default: 1 |