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

# Meta Model

> systematica.api.reports.meta_model

## `MetaModelCVReport`

```python theme={null}
MetaModelCVReport(
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    model: ~BaseStatArb,
    use_close: bool = True,
    freq: str = 'auto',
)
```

Breakdown analysis for meta model, extending `BaseModelBreakdownCV`.

Method generated by attrs for class BaseReportCV.

### Ancestors

* `systematica.api.base.BaseReportCV`
* `systematica.api.base.BaseReportMixin`
* `abc.ABC`

### Methods

#### `score`

```python theme={null}
score(
    self,
    estimator: sklearn.base.BaseEstimator,
    X: pandas.core.frame.DataFrame | numpy.ndarray,
    y: numpy.ndarray | pandas.core.series.Series,
    scoring: str = 'accuracy',
    n_jobs: int = None,
    verbose: int = 0,
) ‑> pandas.core.series.Series
```

Evaluate a score by cross-validation.

This method uses scikit-learn's `cross_val_score` to evaluate the
performance of an estimator on a given dataset using cross-validation.

**See Also**:

* `cross_validate`: To run cross-validation on multiple metrics and also to
  return train scores, fit times and score times.
* `cross_val_predict`: Get predictions from each split of cross-validation for
  diagnostic purposes.
* `sklearn.metrics.make_scorer`: Make a scorer from a performance metric or
  loss function.

**Parameters**:

| Name        | Type            | Default    | Description                                                                                                                                                                                                                                            |
| ----------- | --------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `estimator` | `BaseEstimator` | `--`       | The object to use to fit the data.                                                                                                                                                                                                                     |
| `X`         | `pd.DataFrame`  | `--`       | The data to fit.                                                                                                                                                                                                                                       |
| `y`         | `pd.Series`     | `--`       | The target variable.                                                                                                                                                                                                                                   |
| `scoring`   | `str`           | `accuracy` | The scoring method to use for evaluation. It can be one of the predefined strings in scikit-learn, such as 'accuracy', 'f1', 'roc\_auc', etc., or a custom scoring function that follows the scikit-learn scorer interface. Defaults to `accuracy`.    |
| `n_jobs`    | `int`           | `None`     | Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. Default is `None`. |
| `verbose`   | `int`           | `0`        | Verbosity level. Controls the verbosity of the output during cross-validation. `0` means no output, `1` means progress messages, and higher values increase verbosity. Default is `0`.                                                                 |

**Returns**:

| Type        | Description                                                   |
| ----------- | ------------------------------------------------------------- |
| `pd.Series` | Scores of the estimator for each run of the cross validation. |

## `RollingMetaModelReport`

```python theme={null}
RollingMetaModelReport(
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    model: ~BaseStatArb,
    use_close: bool = True,
    freq: str = 'auto',
)
```

Breakdown analysis for meta model, extending `BaseRollingReport`.

Method generated by attrs for class BaseRollingReport.

### Ancestors

* `systematica.api.base.BaseRollingReport`
* `systematica.api.base.BaseReportMixin`
* `abc.ABC`