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. :