skclean.handlers.WeightedBagging¶
-
class
skclean.handlers.
WeightedBagging
(classifier=None, detector=None, n_estimators=100, replacement=True, sampling_ratio=1.0, n_jobs=1, random_state=None, verbose=0)¶ Similar to regular bagging- except cleaner samples will be chosen more often during bagging. That is, a sample’s probability of getting selected in bootstrapping process is directly proportional to it’s conf_score. See [WCO+18] for details.
- Parameters
classifier (object) – A classifier instance supporting sklearn API. Same as base_estimator of scikit-learn’s BaggingClassifier.
detector (BaseDetector or None, default=None) – To compute conf_score. Set it to None only if conf_score is expected in fit() (e.g. when used inside a Pipeline with a BaseDetector preceding it). Otherwise a Detector must be supplied during instantiation.
n_estimators (int, default=10) – The number of base classifiers in the ensemble.
replacement (bool, default=True) – Whether to sample instances with/without replacement at each base classifier
sampling_ratio (float, 0.0 to 1.0, default=1.0) – No of samples drawn at each tree equals: len(X) * sampling_ratio
n_jobs (int, default=1) – No of parallel cpu cores to use
random_state (int, default=None) – Set this value for reproducibility
verbose (int, default=0) – Controls the verbosity when fitting and predicting
Methods
__init__
([classifier, detector, …])Initialize self.
decision_function
(X)Average of the decision functions of the base classifiers.
fit
(X, y[, conf_score])Build a Bagging ensemble of estimators from the training
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict class for X.
predict_log_proba
(X)Predict class log-probabilities for X.
predict_proba
(X)Predict class probabilities for X.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.
Attributes
classifier
estimators_samples_
The subset of drawn samples for each base estimator.
iterative