Support Vector Classifier

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

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

Ports:

Outputs:

model:

model

Model

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.

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]