hyperimpute.plugins.prediction.regression.plugin_svm module
- class SVMPlugin(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:
RegressionPlugin
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>
- _predict(X: DataFrame, *args: Any, **kwargs: Any) DataFrame
- kernels = ['linear', 'poly', 'rbf', 'sigmoid']
- module_relative_path: Optional[Path]
- static name() str