hyperimpute.plugins.prediction.classifiers.plugin_kneighbors module

class KNeighborsClassifierPlugin(n_neighbors: int = 5, weights: int = 0, algorithm: int = 0, leaf_size: int = 30, p: int = 2, random_state: int = 0, hyperparam_search_iterations: Optional[int] = None, model: Optional[Any] = None, **kwargs: Any)

Bases: ClassifierPlugin

Classification plugin based on the KNeighborsClassifier classifier.

Example

>>> from hyperimpute.plugins.prediction import Predictions
>>> plugin = Predictions(category="classifiers").get("kneighbors")
>>> 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) → KNeighborsClassifierPlugin

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

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

algorithm = ['auto', 'ball_tree', 'kd_tree', 'brute']

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

module_relative_path: Optional[Path]

static name() → str

weights = ['uniform', 'distance']

plugin

alias of KNeighborsClassifierPlugin