> ## 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.

# Multi class

### MultiClassPredictionLabel

Arize class to define the prediction and threshold arguments associated with the multi-class model type.

```python theme={null}
class MultiClassPredictionLabel(
    prediction_scores: Dict[str, Union[float, int]] #required
    threshold_scores: Dict[str, Union[float, int]] = None # optional but required for multi-label use cases
)
```

| Argument            | Data Type                      | Definitions                                                                         |
| ------------------- | ------------------------------ | ----------------------------------------------------------------------------------- |
| `prediction_scores` | `Dict[str, Union[Float, int]]` | (Required) The prediction scores of the classes                                     |
| `threshold_scores`  | `Dict[str, Union[Float, int]]` | (Optional) The threshold scores of the classes. Required for Multi-Label use cases. |

### MultiClassActualLabel

Arize class to define the actual arguments associated with the multi-class model type.

```python theme={null}
class MultiClassActualLabel(
    actual_scores: Dict[str, Union[float, int]] 
)
```

| Argument        | Data Type                      | Definitions                                                                                                            |
| --------------- | ------------------------------ | ---------------------------------------------------------------------------------------------------------------------- |
| `actual_scores` | `Dict[str, Union[Float, int]]` | (Required) The actual scores of the classes. Any class in `actual_scores` with a score of **1** will be sent to Arize. |

### Code Example

```python theme={null}
pred_label = MultiClassPredictionLabel(
	prediction_scores=record["prediction_scores"],
        threshold_scores=record["threshold_scores"],     #additional parameter for multi-label use cases
)
actual_label = MultiClassActualLabel(
	actual_scores={record["actual_class"]: 1},
)
response = arize_client.log(
    model_id="multiclass-classification-multi-label-single-record-ingestion-tutorial",
    model_version="1.0",
    model_type=ModelTypes.MULTI_CLASS,
    environment=Environments.PRODUCTION,
    prediction_id=record["prediction_id"],
    prediction_label=pred_label,
    actual_label=actual_label,
    features=record["features"]
)
```