evalml.pipelines.components.KNeighborsClassifier

class evalml.pipelines.components.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, random_state=0, **kwargs)

K-Nearest Neighbors Classifier.

name = 'KNN Classifier'

model_family = 'k_neighbors'

supported_problem_types = [<ProblemTypes.BINARY: 'binary'>, <ProblemTypes.MULTICLASS: 'multiclass'>, <ProblemTypes.TIME_SERIES_BINARY: 'time series binary'>, <ProblemTypes.TIME_SERIES_MULTICLASS: 'time series multiclass'>]

hyperparameter_ranges = {'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'], 'leaf_size': Integer(low=10, high=30, prior='uniform', transform='identity'), 'n_neighbors': Integer(low=2, high=12, prior='uniform', transform='identity'), 'p': Integer(low=1, high=5, prior='uniform', transform='identity'), 'weights': ['uniform', 'distance']}

default_parameters = {'algorithm': 'auto', 'leaf_size': 30, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}

predict_uses_y = False

Instance attributes

feature_importance	Returns array of 0’s matching the input number of features as feature_importance is not defined for KNN classifiers.
needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Initialize self.
clone	Constructs a new component with the same parameters and random state.
describe	Describe a component and its parameters
fit	Fits component to data
load	Loads component at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves component at file path

Class Inheritance

evalml.pipelines.components.DecisionTreeClassifier.save evalml.pipelines.components.KNeighborsClassifier.__init__