sklearn.svm.libsvm.cross_validation()

X: array-like, dtype=float, size=[n_samples, n_features] :

Y: array, dtype=float, size=[n_samples] :

target vector

svm_type : {0, 1, 2, 3, 4}

Type of SVM: C SVC, nu SVC, one class, epsilon SVR, nu SVR

kernel : {?linear?, ?rbf?, ?poly?, ?sigmoid?, ?precomputed?}

Kernel to use in the model: linear, polynomial, RBF, sigmoid or precomputed.

degree : int

Degree of the polynomial kernel (only relevant if kernel is set to polynomial)

gamma : float

Gamma parameter in RBF kernel (only relevant if kernel is set to RBF)

coef0 : float

Independent parameter in poly/sigmoid kernel.

tol : float

Stopping criteria.

C : float

C parameter in C-Support Vector Classification

nu : float

cache_size : float

random_seed : int, optional

Seed for the random number generator used for probability estimates. 0 by default.