gemseo.mlearning.regression.algos.linreg_settings module#

Settings of the linear regressor.

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

Bases: BaseRegressorSettings

The settings of the linear 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.

Return type:

None

fit_intercept: bool = True#

Whether to fit the intercept.

l2_penalty_ratio: NonNegativeFloat = 1.0#

The penalty ratio related to the l2 regularization.

If 1, use the Ridge penalty. If 0, use the Lasso penalty. Between 0 and 1, use the ElasticNet penalty.

Constraints:
  • ge = 0

penalty_level: NonNegativeFloat = 0.0#

The penalty level greater or equal to 0.

If zero, there is no penalty.

Constraints:
  • ge = 0

random_state: NonNegativeInt | None = 0#

The random state parameter in the case of a penalty.

If None, use the global random state instance from numpy.random. Creating the model multiple times will produce different results. If int, use a new random number generator seeded by this integer. This will produce the same results.