Create setting for the python sklearn SVM (SVC function)

setSVM(
  C = list(1, 0.9, 2, 0.1),
  kernel = list("rbf"),
  degree = list(1, 3, 5),
  gamma = list("scale", 1e-04, 3e-05, 0.001, 0.01, 0.25),
  coef0 = list(0),
  shrinking = list(TRUE),
  tol = list(0.001),
  classWeight = list(NULL),
  cacheSize = 500,
  seed = sample(1e+05, 1)
)

Arguments

C: (list) Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.
kernel: (list) Specifies the kernel type to be used in the algorithm. one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’. If none is given ‘rbf’ will be used.
degree: (list) degree of kernel function is significant only in poly, rbf, sigmoid
gamma: (list) kernel coefficient for rbf and poly, by default 1/n_features will be taken. ‘scale’, ‘auto’ or float, default=’scale’
coef0: (list) independent term in kernel function. It is only significant in poly/sigmoid.
shrinking: (list) whether to use the shrinking heuristic.
tol: (list) Tolerance for stopping criterion.
classWeight: (list) Class weight based on imbalance either 'balanced' or NULL
cacheSize: Specify the size of the kernel cache (in MB).
seed: A seed for the model

Examples

if (FALSE) {
model.svm <- setSVM(kernel='rbf', seed = NULL)
}