class_docstr: A regressor that can operate on mini-batches.
class_docstr_tok: ["A","regressor","that","can","operate","on","mini-batches","."]
docstr: Predict the outcome for each given sample.
  
  Parameters
  ----------
  X
      A dataframe of features.
  
  Returns
  -------
  The predicted outcomes.
docstr_tok: ["Predict","the","outcome","for","each","given","sample",".","Parameters","--","--","--","--","--","X","A","dataframe","of","features",".","Returns","--","--","--","-","The","predicted","outcomes","."]
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 outcomes.
  
      """
  
file_path: river/river/base/regressor.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 MiniBatchRegressor obj's underlying member method code with example usage: obj.predict_many(X) without return types