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

# Metric Output Types

> Understanding the different output types of metrics supported by Luna Studio

## Boolean

Use **Boolean** when your metric output is a binary decision (e.g., “toxic or not”, “adherent or not”).

Set:

* `training.metric.type: "boolean"`

* Your dataset label column should be a binary value.

* The training/evaluation pipeline converts labels to the correct target token.

Example:

```yaml theme={null}
training:
  metric:
    type: "boolean"
  prompt_template: |
    Message:
    {input}

    Respond with "true" or "false".
```

## Categorical (multi-class)

Use **multi-class** when exactly one class should be selected for each example.

Set:

* `training.metric.type: "multi_class"`
* `training.metric.classes`: ordered list of class names

The model is trained to output a **single class key token** (string index): `"0"`, `"1"`, … corresponding to your `classes` ordering.

Example:

```yaml theme={null}
training:
  metric:
    type: "multi_class"
    classes: ["neutral", "positive", "negative"]
  prompt_template: |
    {input}

    Respond with the class key like "0", "1", or "2".
```

* Your dataset labels should be one of the class names (e.g. `"joy"`)
* The pipeline maps those to integer indices using `training.metric.classes`

## Not Supported

These output types are not currently supported by the Luna Studio -

* Count
* Discrete
* Percentage
* Multilabel