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 for each given sample.
  
  Parameters
  ----------
  X
      A dataframe of features.
  
  Returns
  -------
  The predicted labels.
docstr_tok: ["Predict","the","outcome","for","each","given","sample",".","Parameters","--","--","--","--","--","X","A","dataframe","of","features",".","Returns","--","--","--","-","The","predicted","labels","."]
code: def predict_many(self, X: pd.DataFrame) -> pd.Series:
      """Predict the outcome for each given sample.
  
      Parameters
      ----------
      X
          A dataframe of features.
  
      Returns
      -------
      The predicted labels.
  
      """
  
      # The following code acts as a default for each classifier, and may be overridden on an
      # individual basis.
      y_pred = self.predict_proba_many(X)
      if y_pred.empty:
          return y_pred
      return y_pred.idxmax(axis="columns")
  
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 3 for calling the MiniBatchClassifier obj's underlying member method code with example usage: obj.predict_many(X) and returns: y_pred, y_pred