Support Vector Classifier

Support vector machine (SVM) based classifier

Documentation

Support vector machine (SVM) based classifier

Configuration:

• C

  Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.

• 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='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

  • if ‘auto’, uses 1 / n_features.

  Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

• 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, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. Read more in the User Guide <scores_probabilities>.

• shrinking

  Whether to use the shrinking heuristic. See the User Guide <shrinking_svm>.

• 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))

• tol

  Tolerance for stopping criterion.

• max_iter

  Hard limit on iterations within solver, or -1 for no limit.

• random_state

  Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See random_state.

Attributes:

• support_

  Indices of support vectors.

• support_vectors_

  Support vectors.

• n_support_

  Number of support vectors for each class.

• dual_coef_

  Dual coefficients of the support vector in the decision function (see sgd_mathematical_formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-class section of the User Guide for details.

• coef_

  Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

  coef_ is a readonly property derived from dual_coef_ and support_vectors_.

• intercept_

  Constants in decision function.

Input ports:

Output ports:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_svc.SupportVectorClassifier