- LinearClassifier#

LinearClassifier - 1 (


  • name: LinearClassifier (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


Linear classifier


  • classlabels_ints: Class labels when using integer labels. One and only one ‘classlabels’ attribute must be defined.

  • classlabels_strings: Class labels when using string labels. One and only one ‘classlabels’ attribute must be defined.

  • coefficients (required): A collection of weights of the model(s).

  • intercepts: A collection of intercepts.

  • multi_class: Indicates whether to do OvR or multinomial (0=OvR is the default). Default value is 0.

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


  • X (heterogeneous) - T1: Data to be classified.


  • Y (heterogeneous) - T2: Classification outputs (one class per example).

  • Z (heterogeneous) - tensor(float): Classification scores ([N,E] - one score for each class and example

Type Constraints

  • T1 in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]

  • T2 in ( tensor(int64), tensor(string) ): The output will be a tensor of strings or integers.