docstr: Use the machine learning model to make predictions on new data.
  
  Parameters
  ----------
  X : pd.DataFrame
      The input data.
  s : Optional[pd.Series], optional
      The protected attribute.
docstr_tok: ["Use","the","machine","learning","model","to","make","predictions","on","new","data",".","Parameters","--","--","--","--","--","X",":","pd.DataFrame","The","input","data",".","s",":","Optional","[","pd.Series","]",",","optional","The","protected","attribute","."]
code: def predict_proba(
      self,
      X: pd.DataFrame,
      s: Optional[pd.Series] = None,
  ) -> pd.Series:
      """Use the machine learning model to make predictions on new data.
  
      Parameters
      ----------
      X : pd.DataFrame
          The input data.
      s : Optional[pd.Series], optional
          The protected attribute.
      """
      return pd.Series(
          data=self.predict_proba_method(self.reduction)(X)[:, 1],
          name="predictions",
          index=X.index,
      )
  
file_path: aequitas/src/aequitas/fairflow/methods/inprocessing/fairlearn_classifier.py
imports: from copy import deepcopy
  from typing import Any, Callable, Optional, Union
  import numpy
  import pandas
  from fairlearn.reductions import ExponentiatedGradient, GridSearch
  from fairlearn.reductions._grid_search._grid_generator import _GridGenerator
  from ...utils import create_logger, import_object
  from .inprocessing import InProcessing
label_desc: Use image node_id 3 for calling the FairlearnClassifier obj's underlying member method code with example usage: obj.predict_proba(X, s) and returns: pd