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}, 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, poly and sigmoid kernels. Ignored by other kernels. 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.

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 probA, probB : array probability estimates, empty array for probability=False