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:
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