The following options are supported.
Option name |
Type |
Default |
Description |
Constraints |
|---|---|---|---|---|
tau |
real |
\(r=\varepsilon\) |
Numerical stability parameter used in working set selection when kernel is not positive semi definite. |
\(0 \le r\) |
kernel |
string |
\(s=\) rbf |
Kernel function to use for the calculations. |
\(s=\) linear, poly, polynomial, rbf, or sigmoid. |
storage order |
string |
\(s=\) column-major |
Whether data is supplied and returned in row- or column-major order. |
\(s=\) c, column-major, f, fortran, or row-major. |
n_folds |
integer |
\(i=5\) |
Number of folds to use with cross validation. Only used when predict probabilities is enabled. |
\(1 \le i\) |
check data |
string |
\(s=\) no |
Check input data for NaNs prior to performing computation. |
\(s=\) no, or yes. |
cache size |
real |
\(r=-1\) |
Size of the kernel cache in MB. The default value is -1.0 which automatically sets it to a value which will enable storage of the sampled kernel matrix. Increasing value of this option will result in faster training time. |
\(-1 \le r\) |
degree |
integer |
\(i=3\) |
Parameter for ‘polynomial’ kernel. |
\(1 \le i\) |
c |
real |
\(r=1\) |
Regularization parameter. Controls the trade-off between maximizing the margin between classes and minimizing classification errors. A larger value means higher penalty to the loss function on misclassified observations. Applies to SVC, SVR and NuSVR. |
\(0 < r\) |
max_iter |
integer |
\(i=100000\) |
Sets the maximum number of iterations. Use 0 to specify no limit. |
\(0 \le i\) |
nu |
real |
\(r=0.5\) |
An upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Applies to NuSVC and NuSVR. |
\(0 < r \le 1\) |
coef0 |
real |
\(r=0\) |
Constant in ‘polynomial’ and ‘sigmoid’ kernels. |
There are no constraints on \(r\). |
tolerance |
real |
\(r=10^{-3}\) |
Convergence tolerance. |
\(0 < r\) |
seed |
integer |
\(i=0\) |
Seed for random number generation; set to -1 for non-deterministic results. |
\(-1 \le i\) |
epsilon |
real |
\(r=0.1\) |
Defines the tolerance for errors in predictions by creating an acceptable margin (tube) within which errors are not penalized. Applies to SVR |
\(0 \le r\) |
max_ws_size |
integer |
\(i=-1\) |
Specifies the maximum working set size. A value divisible by 64 is recommended for optimal performance. Setting -1 automatically selects the optimal size based on the input data. |
\(-1 \le i\) |
predict probabilities |
integer |
\(i=0\) |
Evaluate class probabilities (in addition to class predictions).Needs to be 1 if calls to predict_proba or predict_log_probaare made after fit. |
\(0 \le i \le 1\) |
gamma |
real |
\(r=-1\) |
Parameter for ‘rbf’, ‘polynomial’, and ‘sigmoid’ kernels. If the value is less than 0, it is set to 1/(n_features * Var(X)). |
\(-1 \le r\) |