sklearn.svm.libsvm.fit()

X : array-like, dtype=float64, size=[n_samples, n_features]

Y : array, dtype=float64, size=[n_samples]

target vector

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

Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR respectively. 0 by default.

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

Kernel to use in the model: linear, polynomial, RBF, sigmoid or precomputed. ?rbf? by default.

degree : int32, optional

Degree of the polynomial kernel (only relevant if kernel is set to polynomial), 3 by default.

gamma : float64, optional

Gamma parameter in RBF kernel (only relevant if kernel is set to RBF). 0.1 by default.

coef0 : float64, optional

Independent parameter in poly/sigmoid kernel. 0 by default.

tol : float64, optional

Numeric stopping criterion (WRITEME). 1e-3 by default.

C : float64, optional

C parameter in C-Support Vector Classification. 1 by default.

nu : float64, optional

0.5 by default.

epsilon : double, optional

0.1 by default.

class_weight : array, dtype float64, shape (n_classes,), optional

np.empty(0) by default.

sample_weight : array, dtype float64, shape (n_samples,), optional

np.empty(0) by default.

shrinking : int, optional

1 by default.

probability : int, optional

0 by default.

cache_size : float64, optional

Cache size for gram matrix columns (in megabytes). 100 by default.

max_iter : int (-1 for no limit), optional.

Stop solver after this many iterations regardless of accuracy (XXX Currently there is no API to know whether this kicked in.) -1 by default.

random_seed : int, optional

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