skclean.detectors.MCS

class skclean.detectors.MCS(classifier=None, n_steps=20, n_jobs=1, random_state=None)

Detects noise using a sequential Markov Chain Monte Carlo sampling algorithm. Tested for binary classification, multi-class classification sometimes perform poorly. See [ZCTP19] for details.

Parameters
  • classifier (object, default=None) – A classifier instance supporting sklearn API. If None, LogisticRegression is used.

  • n_steps (int, default=20) – No of sampling steps to run.

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

  • random_state (int, default=None) – Set this value for reproducibility

Methods

__init__([classifier, n_steps, 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)