- LinearRegressor#

LinearRegressor - 1 (


  • name: LinearRegressor (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


Generalized linear regression evaluation.

If targets is set to 1 (default) then univariate regression is performed.

If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.

The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets.


  • coefficients: Weights of the model(s).

  • intercepts: Weights of the intercepts, if used.

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

  • targets: The total number of regression targets, 1 if not defined. Default value is 1.


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


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

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input must be a tensor of a numeric type.