> ## 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.analytics.meta_model ## `MetaModelCV` ```python theme={null} MetaModelCV( id: str = None, loader_or_data: Callable = , timeframe: str = '1d', start: str = None, end: str = None, s1: str = 'BTCUSDT', s2: str = 'ETHUSDT', suffix: str = 'h5', use_close: bool = True, freq: str = None, signal_model: str = None, long_entries: float = nan, long_exits: float = nan, short_entries: float = nan, short_exits: float = nan, clean: bool = True, select_symbols: str | int | List[str | int] = None, portfolio_config: Dict[str, Any] = _Nothing.NOTHING, model_params: Dict[str, Any] = _Nothing.NOTHING, metrics: str | List[str] = None, metrics_kwargs: Dict[str, Any] = _Nothing.NOTHING, use_rolling: bool = False, storage: str | optuna.storages._base.BaseStorage = None, pruner: optuna.pruners._base.BasePruner = , sampler: optuna.samplers._base.BaseSampler = , study_name: str = None, direction: str | List[str] = 'maximize', load_if_exists: bool = False, n_trials: int = 100, n_completed_trials: int = None, timeout: float = None, n_jobs: int = 1, catch: Iterable[Type[Exception]] | Type[Exception] = _Nothing.NOTHING, callbacks: Iterable[Callable[[optuna.study.study.Study, optuna.trial._frozen.FrozenTrial], None]] | None = None, gc_after_trial: bool = False, show_progress_bar: bool = False, verbose: bool = True, tracker: Type = None, model: Any = None, preprocess_func: Callable = None, ) ``` Breakdown analysis for meta model, extending `BaseModelBreakdownCV`. Method generated by attrs for class WalkForwardCV. ### Ancestors * `systematica.walk_forward.cv.WalkForwardCV` * `systematica.walk_forward.base.BaseWalkForward` * `abc.ABC` * `systematica.generic.base.Model` * `systematica.generic.base.Feature` ### 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. | ## `MetaModelRolling` ```python theme={null} MetaModelRolling( id: str = None, loader_or_data: Callable = , timeframe: str = '1d', start: str = None, end: str = None, s1: str = 'BTCUSDT', s2: str = 'ETHUSDT', suffix: str = 'h5', use_close: bool = True, freq: str = None, signal_model: str = None, long_entries: float = nan, long_exits: float = nan, short_entries: float = nan, short_exits: float = nan, clean: bool = True, select_symbols: str | int | List[str | int] = None, portfolio_config: Dict[str, Any] = _Nothing.NOTHING, model_params: Dict[str, Any] = _Nothing.NOTHING, metrics: str | List[str] = None, metrics_kwargs: Dict[str, Any] = _Nothing.NOTHING, use_rolling: bool = False, storage: str | optuna.storages._base.BaseStorage = None, pruner: optuna.pruners._base.BasePruner = , sampler: optuna.samplers._base.BaseSampler = , study_name: str = None, direction: str | List[str] = 'maximize', load_if_exists: bool = False, n_trials: int = 100, n_completed_trials: int = None, timeout: float = None, n_jobs: int = 1, catch: Iterable[Type[Exception]] | Type[Exception] = _Nothing.NOTHING, callbacks: Iterable[Callable[[optuna.study.study.Study, optuna.trial._frozen.FrozenTrial], None]] | None = None, gc_after_trial: bool = False, show_progress_bar: bool = False, verbose: bool = True, tracker: Type = None, model: Any = None, preprocess_func: Callable = None, ) ``` Breakdown analysis for meta model, extending `BaseWalkForward`. Method generated by attrs for class RollingWalkForward. ### Ancestors * `systematica.walk_forward.rolling.RollingWalkForward` * `systematica.walk_forward.base.BaseWalkForward` * `abc.ABC` * `systematica.generic.base.Model` * `systematica.generic.base.Feature`