skclean.handlers.Costing¶
-
class
skclean.handlers.
Costing
(classifier=None, detector=None, n_estimators=100, n_jobs=1, random_state=None, verbose=0)¶ Implements costing, a method combining cost-proportionate rejection sampling and ensemble aggregation. At each base classifier, samples are selected for training with probability equal to conf_score. See [ZLA03] 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.
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