gemseo.mlearning.classification.algos.svm_settings module

Settings of the SVM classification algorithm.

Settings SVMClassifier_Settings(*, transformer=None, parameters=None, input_names=(), output_names=(), C=1.0, kernel='rbf', probability=False, random_state=0)

Bases: BaseClassifierSettings

The settings of the SV classification algorithm.

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 ().

• C (Annotated[float, Gt(gt=0)]) --

  By default it is set to 1.0.

• kernel (str | Callable[[], Callable]) --

  By default it is set to "rbf".

• probability (bool) --

  By default it is set to False.

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

  By default it is set to 0.

Return type:

None

C: PositiveFloat = 1.0

The inverse L2 regularization parameter.

Constraints:

• gt = 0

kernel: str | Annotated[Callable, WithJsonSchema({})] = 'rbf'

The name of the kernel or a callable for the SVM.

Examples of names: "linear", "poly", "rbf", "sigmoid", "precomputed".

probability: bool = False

Whether to enable the probability estimates.

random_state: NonNegativeInt | None = 0

The random state parameter.

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.