- SVMClassifier

SVMClassifier - 1 (


  • name: SVMClassifier (GitHub)

  • domain:

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 1 of domain


Support Vector Machine classifier


  • classlabels_ints - INTS :

    Class labels if using integer labels.
    One and only one of the ‘classlabels_*’ attributes must be defined.

  • classlabels_strings - STRINGS :

    Class labels if using string labels.
    One and only one of the ‘classlabels_*’ attributes must be defined.

  • coefficients - FLOATS :

  • kernel_params - FLOATS :

    List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.

  • kernel_type - STRING (default is 'LINEAR'):

    The kernel type, one of ‘LINEAR,’ ‘POLY,’ ‘RBF,’ ‘SIGMOID’.

  • post_transform - STRING (default is 'NONE'):

    Indicates the transform to apply to the score.

  • prob_a - FLOATS :

    First set of probability coefficients.

  • prob_b - FLOATS :

    Second set of probability coefficients. This array must be same size as prob_a.
    If these are provided then output Z are probability estimates, otherwise they are raw scores.

  • rho - FLOATS :

  • support_vectors - FLOATS :

  • vectors_per_class - INTS :


  • X (heterogeneous) - T1:

    Data to be classified.


  • Y (heterogeneous) - T2:

    Classification outputs (one class per example).

  • Z (heterogeneous) - tensor(float):

    Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.

Type Constraints

  • T1 in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ):

    The input must be a tensor of a numeric type, either [C] or [N,C].

  • T2 in ( tensor(int64), tensor(string) ):

    The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.