class_docstr: Reject option classification is a postprocessing technique that gives
  favorable outcomes to unpriviliged groups and unfavorable outcomes to
  priviliged groups in a confidence band around the decision boundary with the
  highest uncertainty [10]_.
  
  References:
      .. [10] F. Kamiran, A. Karim, and X. Zhang, "Decision Theory for
         Discrimination-Aware Classification," IEEE International Conference
         on Data Mining, 2012.
class_docstr_tok: ["Reject","option","classification","is","a","postprocessing","technique","that","gives","favorable","outcomes","to","unpriviliged","groups","and","unfavorable","outcomes","to","priviliged","groups","in","a","confidence","band","around","the","decision","boundary","with","the","highest","uncertainty","[","10","]","_.","References",":","..","[","10","]","F.","Kamiran",",","A.","Karim",",","and","X.","Zhang",",","``","Decision","Theory","for","Discrimination-Aware","Classification",",","''","IEEE","International","Conference","on","Data","Mining",",","2012","."]
docstr: fit and predict methods sequentially.
docstr_tok: ["fit","and","predict","methods","sequentially","."]
code: def fit_predict(self, dataset_true, dataset_pred):
      """fit and predict methods sequentially."""
      return self.fit(dataset_true, dataset_pred).predict(dataset_pred)
  
file_path: AIF360/aif360/algorithms/postprocessing/reject_option_classification.py
imports: import numpy
  from warnings import warn
  from aif360.algorithms import Transformer
  from aif360.metrics import utils
  from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric
label_desc: Use image node_id 4 for calling the RejectOptionClassification obj's underlying member method code with example usage: obj.fit_predict(dataset_true, dataset_pred) and returns: self