skclean.detectors.InstanceHardness¶
-
class
skclean.detectors.
InstanceHardness
(classifiers=None, cv=None, n_jobs=1, random_state=None)¶ A set of classifiers are used to predict labels of each sample using cross-validation. conf_score of a sample is percentage classifiers that correctly predict it’s label. See [SMGC14] for details.
- Parameters
classifiers (list, default=None) – Classifiers used to predict sample labels. If None, four classifiers are used: GaussianNB, DecisionTreeClassifier, KNeighborsClassifier and LogisticRegression.
cv (int, cross-validation generator or an iterable, default=None) – If None, uses 5-fold stratified k-fold if int, no of folds to use in stratified k-fold
n_jobs (int, default=1) – No of parallel cpu cores to use
random_state (int, default=None) – Set this value for reproducibility
Methods
__init__
([classifiers, cv, n_jobs, random_state])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)Attributes
DEFAULT_CLFS