skclean.detectors.RandomForestDetector¶
-
class
skclean.detectors.
RandomForestDetector
(n_estimators=101, sampling_ratio=None, n_jobs=1, random_state=None)¶ Trains a Random Forest first- for each sample, only trees that didn’t select it for training (via bootstrapping) are used to predict it’s label. Percentage of trees that correctly predicted the label is the sample’s conf_score.
See [SMartinezMunozSuarez18] for details.
- n_estimatorsint, default=101
No of trees in Random Forest.
- sampling_ratiofloat, 0.0 to 1.0, default=1.0
No of samples drawn at each tree equals: len(X) * sampling_ratio
- n_jobsint, default=1
No of parallel cpu cores to use
- random_stateint, default=None
Set this value for reproducibility
Methods
__init__
([n_estimators, sampling_ratio, …])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)