(l-onnx-docai-onnx-ml-SVMRegressor)=

# ai.onnx.ml - SVMRegressor


(l-onnx-opai-onnx-ml-svmregressor-1)=

## SVMRegressor - 1 (ai.onnx.ml)

### Version

- **name**: [SVMRegressor (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators-ml.md#ai.onnx.ml.SVMRegressor)
- **domain**: `ai.onnx.ml`
- **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 ai.onnx.ml**.

### Summary

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

### Attributes

* **coefficients - FLOATS** :

  Support vector coefficients.

* **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'.

* **n_supports - INT** (default is `'0'`):

  The number of support vectors.

* **one_class - INT** (default is `'0'`):

  Flag indicating whether the regression is a one-class SVM or not.

* **post_transform - STRING** (default is `'NONE'`):

  Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'

* **rho - FLOATS** :

* **support_vectors - FLOATS** :

  Chosen support vectors

### Inputs

- **X** (heterogeneous) - **T**:

  Data to be regressed.

### Outputs

- **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].