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

# Generate risk

### Create prediction function

Refer to [Prediction Section](../prediction/versioning) on to how to create a prediction function and add it to a version of a model.

### Create validation dataset

SuperAlign expects the dataset version to be used for validation in a specific manner.

### Run your first evaluation

```python theme={null}
from pureml.decorators import dataset

@dataset("<dataset_name>")
def create_validation_dataset():
    x_test = #Data for testing
    y_test = #Labels for testing
    A_test = #Sensitive features (optional)

    return {"x_test":x_test, "y_test":y_test, sensitive_features: "A_test"}
```

<Info>
  Any dataset that is intended to be used in model validation should be
  registered in a dictionary format with mandatory keys; "x\_test" for testing
  data, and "y\_test" for dataset labels.
</Info>

### Running Evaluator

```python theme={null}
from pureml_evaluate.policy import policy_eval
results = policy_eval.eval(label_model='Credit Underwriting:v1',
            label_dataset='Credit Loan Dataset:v1')

```

<Info>
  SuperAlign supports two task types for evaluation, "classification", and
  "regression".
</Info>

After running the evaluator, the computed results were sent to the SuperAlign Backend. This will allow to apply any policy from the Dashboard itself.