gemseo.mlearning.linear_model_fitting.omp_cv_settings module#

Settings for the scikit-learn Orthogonal Matching Pursuit (OMP) algorithm with build-in cross-validation.

Settings OrthogonalMatchingPursuitCV_Settings(*, fit_intercept=True, max_iter=None, cv=None, verbose=False)[source]#

Bases: BaseLinearModelFitter_Settings

Settings for the scikit-learn Orthogonal Matching Pursuit (OMP) algorithm with build-in cross-validation.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • fit_intercept (bool) --

    By default it is set to True.

  • max_iter (Annotated[int, Gt(gt=0)] | None)

  • cv (int | None)

  • verbose (bool) --

    By default it is set to False.

Return type:

None

cv: int | None = None#

The number of folds. If None, use the efficient Leave-One-Out cross-validation.

max_iter: PositiveInt | None = None#

The maximum numbers of iterations to perform, therefore maximum features to include. 10% of n_features but at least 5 if available.

verbose: bool = False#

Sets the verbosity amount.