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

# Utils.Types.ModelTypes

<CardGroup>
  <Card title="View source on Github" href="https://github.com/Arize-ai/client_python/blob/9d02774fd28196d65485eb80544f51e25a73148f/src/arize/utils/types.py#L35" icon="github" horizontal />
</CardGroup>

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

### Model Types

```python theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
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](/docs/ax/machine-learning/machine-learning/use-cases-ml/regression) | `ModelTypes.REGRESSION` | Regression models predict continuous values |
| [Binary Classification](/docs/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 |
| [Multi Class](/docs/ax/machine-learning/machine-learning/use-cases-ml/multiclass-classification) | `ModelType.MULTI_CLASS` | Multiclass models predict multiple categorical values |
| [Ranking](/docs/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 |
| [Natural Language Processing (NLP)](/docs/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)](/docs/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) |
| [Object Detection](/docs/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={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
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
)
```