> ## 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.Metrics

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  <Card title="View source on Github" href="https://github.com/Arize-ai/client_python/blob/576b5e62ac11124dd27201bc3db7587c46d9a371/arize/utils/types.py#L38" icon="github" horizontal />
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Arize enum for metrics grouping when validating a schema column in [`log()`](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/logpage) call.

| Use Case       | SDK Metric               | Metric                                                              |
| -------------- | ------------------------ | ------------------------------------------------------------------- |
| Regression     | `Metrics.REGRESSION`     | MAPE, MAE, RMSE, MSE, R-Squared, Mean Error                         |
| Classification | `Metrics.CLASSIFICATION` | Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity |
| Ranking        | `Metrics.RANKING`        | NDCG                                                                |
| AUC & LogLoss  | `Metrics.AUC_LOG_LOSS`   | AUC, PR-AUC, Log Loss                                               |
| Ranking Label  | `Metrics.RANKING_LABEL`  | GroupAUC, MAP, MRR                                                  |

### Method

`repr()`

To view applicable metrics, pass in your desired **SDK Metric** from above.

```python theme={null}
repr(Metrics.[SDK Metric])
```

### Code Example

```javascript theme={null}
response = arize_client.log(
    model_id='sample-binary-classification-model', 
    ...
    metrics_validation=[Metrics.CLASSIFICATION]

)

response = arize_client.log(
    model_id='sample-regression-model', 
    ...
    metrics_validation=[Metrics.REGRESSION]
)

response = arize_client.log(
    model_id='sample-ranking-model', 
    ...
    metrics_validation=[Metrics.RANKING, Metrics.RANKING_LABEL]
)
```