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.

class node_svc.SupportVectorClassifier

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