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)