.. _`Support Vector Classifier`:

.. _`org.sysess.sympathy.machinelearning.svc`:

Support Vector Classifier
~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: svm.svg
   :width: 48


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.

    .. versionchanged:: 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 :ref:`multi-class section of the User Guide
    <svm_multi_class>` 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*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_svc
    :noindex:

.. class:: SupportVectorClassifier
    :noindex: