Support Vector Classifier¶
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.
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class
node_svc.
SupportVectorClassifier
[source]¶ Support vector machine (SVM) based classifier
Configuration: C
Penalty parameter C of the error term.
kernel
Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape
(n_samples, n_samples)
.degree
Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
gamma
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
coef0
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
probability
Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.
shrinking
Whether to use the shrinking heuristic.
tol
Tolerance for stopping criterion.
class_weight
Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
max_iter
Hard limit on iterations within solver, or -1 for no limit.
random_state
The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Attributes: support_
Indices of support vectors.
support_vectors_
Support vectors.
n_support_
Number of support vectors for each class.
dual_coef_
Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the section about multi-class classification in the SVM section of the User Guide for details.
coef_
Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the section about multi-class classification in the SVM section of the User Guide for details.
intercept_
Constants in decision function.
Inputs: Outputs: - model : model
Model