pmdarima.model_selection.cross_val_score
- pmdarima.model_selection.cross_val_score(estimator, y, X=None, scoring=None, cv=None, verbose=0, error_score=nan)[source][source]
Evaluate a score by cross-validation
- Parameters:
estimator : estimator
An estimator object that implements the
fitmethody : array-like or iterable, shape=(n_samples,)
The time-series array.
X : array-like, shape=[n_obs, n_vars], optional (default=None)
An optional 2-d array of exogenous variables.
scoring : str or callable, optional (default=None)
The scoring metric to use. If a callable, must adhere to the signature
metric(true, predicted). Valid string scoring metrics include:‘smape’
‘mean_absolute_error’
‘mean_squared_error’
cv : BaseTSCrossValidator or None, optional (default=None)
An instance of cross-validation. If None, will use a RollingForecastCV
verbose : integer, optional
The verbosity level.
error_score : ‘raise’ or numeric
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, ModelFitWarning is raised. This parameter does not affect the refit step, which will always raise the error.
