Support Vector Classifier

../../../../_images/svm.svg

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

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