class_docstr: A classifier that can operate on mini-batches.
class_docstr_tok: ["A","classifier","that","can","operate","on","mini-batches","."]
docstr: Predict the outcome probabilities for each given sample.
  
  Parameters
  ----------
  X
      A dataframe of features.
  
  Returns
  -------
  A dataframe with probabilities of `True` and `False` for each sample.
docstr_tok: ["Predict","the","outcome","probabilities","for","each","given","sample",".","Parameters","--","--","--","--","--","X","A","dataframe","of","features",".","Returns","--","--","--","-","A","dataframe","with","probabilities","of","`","True","`","and","`","False","`","for","each","sample","."]
code: def predict_proba_many(self, X: pd.DataFrame) -> pd.DataFrame:
      """Predict the outcome probabilities for each given sample.
  
      Parameters
      ----------
      X
          A dataframe of features.
  
      Returns
      -------
      A dataframe with probabilities of `True` and `False` for each sample.
  
      """
  
      # Some classifiers don't have the ability to output probabilities, and instead only
      # predict labels directly. Therefore, we cannot impose predict_proba_many as an abstract
      # method that each classifier has to implement. Instead, we raise an exception to indicate
      # that a classifier does not support predict_proba_many.
      raise NotImplementedError
  
file_path: river/river/base/classifier.py
imports: from __future__ import annotations
  import abc
  import typing
  from river import base
  from .None import estimator
label_desc: Use image node_id 2 for calling the MiniBatchClassifier obj's underlying member method code with example usage: obj.predict_proba_many(X) without return types