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)