hyperimpute.plugins.prediction.regression.plugin_mlp_regressor module

class MLPRegressionPlugin(model: Optional[Any] = None, random_state: int = 0, **kwargs: Any)

Bases: RegressionPlugin

Regression plugin based on the MLP Regression classifier.

Example

>>> from hyperimpute.plugins.prediction import Predictions
>>> plugin = Predictions(category="regression").get("mlp_regressor")
>>> 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) → MLPRegressionPlugin

_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

plugin

alias of MLPRegressionPlugin