8.24.3.1. sklearn.svm.libsvm.fit

sklearn.svm.libsvm.fit()

Train the model using libsvm (low-level method)

Parameters :

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}

Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR respectevely.

kernel : {‘linear’, ‘rbf’, ‘poly’, ‘sigmoid’, ‘precomputed’}

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

degree : int32

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

gamma : float64

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

coef0 : float64

Independent parameter in poly/sigmoid kernel.

tol : float64

Stopping criteria.

C : float64

C parameter in C-Support Vector Classification

nu : float64

cache_size : float64

Returns :

support : array, shape=[n_support]

index of support vectors

support_vectors : array, shape=[n_support, n_features]

support vectors (equivalent to X[support]). Will return an empty array in the case of precomputed kernel.

n_class_SV : array

number of support vectors in each class.

sv_coef : array

coefficients of support vectors in decision function.

intercept : array

intercept in decision function

label : labels for different classes (only relevant in classification).

probA, probB : array

probability estimates, empty array for probability=False