skclean.detectors.PartitioningDetector

class skclean.detectors.PartitioningDetector(classifier=None, n_partitions=5, n_jobs=1, random_state=None)

Partitions dataset into n subsets, trains a classifier on each. Trained models are then used to predict on entire dataset.

See [KR07] for details.

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

  • n_partitions (int, default=5) – No of non-overlapping partitions created from dataset. For small datasets, you might want to use smaller values.

  • 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_partitions, 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)