hyperimpute.plugins.prediction.classifiers.plugin_svc module

class SVCPlugin(kernel: int = 0, tol: float = 0.001, C: float = 1.0, max_iter: int = -1, hyperparam_search_iterations: Optional[int] = None, random_state: int = 0, **kwargs: Any)

Bases: ClassifierPlugin

Regression plugin based on the SVM.

Example

>>> from hyperimpute.plugins.prediction import Predictions
>>> plugin = Predictions(category="regression").get("svm")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y) # returns the probabilities for each class

_abc_impl = <_abc_data object>

_fit(X: DataFrame, *args: Any, **kwargs: Any) → SVCPlugin

_predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

_predict_proba(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

static hyperparameter_space(*args: Any, **kwargs: Any) → List[Params]

kernels = ['linear', 'poly', 'rbf', 'sigmoid']

module_relative_path: Optional[Path]

static name() → str

plugin: alias of SVCPlugin