hyperimpute.plugins.prediction.regression.plugin_linear_regression module

class LinearRegressionPlugin(solver: int = 0, max_iter: Optional[int] = 10000, tol: float = 0.001, hyperparam_search_iterations: Optional[int] = None, random_state: int = 0, **kwargs: Any)

Bases: RegressionPlugin

Regression plugin based on the Linear Regression.

Example

>>> from hyperimpute.plugins.prediction import Predictions
>>> plugin = Predictions(category="regression").get("linear_regression")
>>> 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) → LinearRegressionPlugin

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

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

module_relative_path: Optional[Path]

static name() → str

solvers = ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']

plugin: alias of LinearRegressionPlugin