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

# Evaluation

> Model performance evaluation

### `evaluate`

```python theme={null}
evaluate(df, metrics, models=None, train_df=None, level=None, id_col='unique_id', time_col='ds', target_col='y', cutoff_col='cutoff', agg_fn=None)
```

Evaluate forecast using different metrics.

**Parameters:**

| Name         | Type                                                 | Description                                                                                                                             | Default                   |
| ------------ | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`         | <code>pandas, polars, dask or spark DataFrame</code> | Forecasts to evaluate. Must have `id_col`, `time_col`, `target_col` and models' predictions.                                            | *required*                |
| `metrics`    | <code>list of callable</code>                        | Functions with arguments `df`, `models`, `id_col`, `target_col` and optionally `train_df`.                                              | *required*                |
| `models`     | <code>list of str</code>                             | Names of the models to evaluate. If `None` will use every column in the dataframe after removing id, time and target. Defaults to None. | <code>None</code>         |
| `train_df`   | <code>pandas, polars, dask or spark DataFrame</code> | Training set. Used to evaluate metrics such as `mase`. Defaults to None.                                                                | <code>None</code>         |
| `level`      | <code>list of int</code>                             | Prediction interval levels. Used to compute losses that rely on quantiles. Defaults to None.                                            | <code>None</code>         |
| `id_col`     | <code>[str](#str)</code>                             | Column that identifies each serie. Defaults to 'unique\_id'.                                                                            | <code>'unique\_id'</code> |
| `time_col`   | <code>[str](#str)</code>                             | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'.                                       | <code>'ds'</code>         |
| `target_col` | <code>[str](#str)</code>                             | Column that contains the target. Defaults to 'y'.                                                                                       | <code>'y'</code>          |
| `cutoff_col` | <code>[str](#str)</code>                             | Column that identifies the cutoff point for each forecast cross-validation fold. Defaults to 'cutoff'.                                  | <code>'cutoff'</code>     |
| `agg_fn`     | <code>[str](#str)</code>                             | Statistic to compute on the scores by id to reduce them to a single number. Defaults to None.                                           | <code>None</code>         |

**Returns:**

| Type                                                      | Description                                                                                                                                                                       |
| --------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[AnyDFType](#utilsforecast.compat.AnyDFType)</code> | pandas, polars, dask or spark DataFrame: Metrics with one row per (id, metric) combination and one column per model. If `agg_fn` is not `None`, there is only one row per metric. |