> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Utils.Types.ModelTypes

<img className="inline m-0" src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/9e9834e4-image.jpeg" /> [View source on Github](https://github.com/Arize-ai/client_python/blob/main/src/arize/utils/types.py#L10)

Arize enum to specify your model type represented in the platform and validate applicable performance metrics.

### Model Types

```python theme={null}
from arize.utils.types import ModelTypes
```

Specify a `model_type` when logging a prediction for the first time for a new model.

#### Method

`list_types()`

View Source

Returns a list of all model types.

```
ModelTypes.list_types()
```

| Use Case                                                                                                                | SDK ModelType                     | Description                                                                                                                                                 |
| ----------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Regression](/ax/machine-learning/machine-learning/use-cases-ml/regression)                                             | `ModelTypes.REGRESSION`           | Regression models predict continuous values                                                                                                                 |
| [Binary Classification](/ax/machine-learning/machine-learning/use-cases-ml/binary-classification)                       | `ModelType.BINARY_CLASSIFICATION` | Binary classification models predict only two categorical values, typically represented as 0 or 1                                                           |
| [Ranking](/ax/machine-learning/machine-learning/use-cases-ml/ranking)                                                   | `ModelType.RANKING`               | Ranking models predict the relative ordering of a set of items based on their features                                                                      |
| [Multi Class](/ax/machine-learning/machine-learning/use-cases-ml/multiclass-classification)                             | `ModelType.MULTI_CLASS`           | Multiclass models predict multiple categorical values                                                                                                       |
| [Natural Language Processing (NLP)](/ax/machine-learning/machine-learning/use-cases-ml/natural-language-processing-nlp) | `ModelType.SCORE_CATEGORICAL`     | NLP models are categorical models specifically designed to work with text data and perform various tasks (i.e. sentiment analysis and language translation) |
| [Computer Vision (CV)](/ax/machine-learning/computer-vision/use-cases-cv/computer-vision-cv)                            | `ModelType.SCORE_CATEGORICAL`     | CV models are categorical models specifically designed to work with visual data and perform various tasks (i.e. object detection and image classification)  |
| Large Language Models (LLMs)                                                                                            | `ModelTypes.GENERATIVE_LLM`       | Models that use vast amounts of data to generate human-like language and perform a wide range of natural language processing tasks                          |
| [Object Detection](/ax/machine-learning/computer-vision/use-cases-cv/object-detection)                                  | `ModelTypes.OBJECT_DETECTION`     | Object detection models identify and locate objects within images or videos by assigning them specific bounding boxes                                       |

### Code Example

```javascript theme={null}
response = arize_client.log(
  (model_id = "sample-binary-classification-model"),
  ...(model_type = ModelTypes.BINARY_CLASSIFICATION)
);

response = arize_client.log(
  (model_id = "sample-regression-model"),
  ...(model_type = ModelTypes.REGRESSION)
);

response = arize_client.log(
  (model_id = "sample-ranking-model"),
  ...(model_type = ModelTypes.RANKING)
);
```