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

  • classlabels_strings: Class labels if using string labels.<br>One and only one of the ‘classlabels_*’ attributes must be defined.

  • coefficients:

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

  • kernel_type: The kernel type, one of ‘LINEAR,’ ‘POLY,’ ‘RBF,’ ‘SIGMOID’. Default value is 'LINEAR'.

  • post_transform: Indicates the transform to apply to the score. <br>One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’ Default value is 'NONE'.

  • prob_a: First set of probability coefficients.

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

  • rho:

  • support_vectors:

  • vectors_per_class:


  • 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 the classlabels_* attributes is used. Its size will match the bactch size of the input.