- SVMRegressor#

SVMRegressor - 1 (


  • name: SVMRegressor (GitHub)

  • domain:

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

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


Support Vector Machine regression prediction and one-class SVM anomaly detection.


  • coefficients: Support vector 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'.

  • n_supports: The number of support vectors. Default value is 0.

  • one_class: Flag indicating whether the regression is a one-class SVM or not. Default value is 0.

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

  • rho:

  • support_vectors: Chosen support vectors


  • X (heterogeneous) - T: Data to be regressed.


  • Y (heterogeneous) - tensor(float): Regression outputs (one score per target per example).

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input type must be a tensor of a numeric type, either [C] or [N,C].