skclean.detectors.KDN

class skclean.detectors.KDN(n_neighbors=5, weight='uniform', n_jobs=1)

For each sample, the percentage of it’s nearest neighbors with same label serves as it’s conf_score. Euclidean distance is used to find the nearest neighbors. See [WCO+18][SMGC14] for details.

Parameters
  • n_neighbors (int, default=5) – No of nearest neighbors to use to compute conf_score

  • weight (string, default='uniform') – weight function used in prediction. If ‘uniform’, all points in each neighborhood are weighted equally. If ‘distance’, weights points by the inverse of their distance.

  • n_jobs (int, default=1) – No of parallel cpu cores to use

Methods

__init__([n_neighbors, weight, n_jobs])

Initialize self.

detect(X, y)

fit_transform(X[, y])

Fit to data, then transform it.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

transform(X)