8.24.3.3. sklearn.svm.libsvm.predict

sklearn.svm.libsvm.predict()

Predict target values of X given a model (low-level method)

Parameters :

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

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.

eps : float

Stopping criteria.

C : float

C parameter in C-Support Vector Classification

Returns :

dec_values : array

predicted values.

TODO: probably there’s no point in setting some parameters, like :

cache_size or weights. :