gemseo.mlearning.regression.algos.polyreg_settings module#

Settings of the polynomial regressor.

Settings PolynomialRegressor_Settings(*, transformer=None, parameters=None, input_names=(), output_names=(), fit_intercept=True, penalty_level=0.0, l2_penalty_ratio=1.0, random_state=0, degree=True)[source]#

Bases: LinearRegressor_Settings

The settings of the polynomial regressor.

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:
  • transformer (Mapping[str, Any])

  • parameters (Mapping[str, Any])

  • input_names (Sequence[str]) --

    By default it is set to ().

  • output_names (Sequence[str]) --

    By default it is set to ().

  • fit_intercept (bool) --

    By default it is set to True.

  • penalty_level (Annotated[float, Ge(ge=0)]) --

    By default it is set to 0.0.

  • l2_penalty_ratio (Annotated[float, Ge(ge=0)]) --

    By default it is set to 1.0.

  • random_state (Annotated[int, Ge(ge=0)] | None) --

    By default it is set to 0.

  • degree (Annotated[int, Gt(gt=0)]) --

    By default it is set to True.

Return type:

None

degree: PositiveInt = True#

The polynomial degree.

Constraints:
  • gt = 0