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

# ai.onnx.ml - LinearClassifier


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

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

### Version

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

### Summary

Linear classifier

### Attributes

* **classlabels_ints - INTS** :

  Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.

* **classlabels_strings - STRINGS** :

  Class labels when using string labels. One and only one 'classlabels' attribute must be defined.

* **coefficients - FLOATS** (required) :

  A collection of weights of the model(s).

* **intercepts - FLOATS** :

  A collection of intercepts.

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

  Indicates whether to do OvR or multinomial (0=OvR is the default).

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

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

### Inputs

- **X** (heterogeneous) - **T1**:

  Data to be classified.

### Outputs

- **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.