class_docstr: A classifier that can operate on mini-batches.
class_docstr_tok: ["A","classifier","that","can","operate","on","mini-batches","."]
docstr: Update the model with a mini-batch of features `X` and boolean targets `y`.
  
  Parameters
  ----------
  X
      A dataframe of features.
  y
      A series of boolean target values.
docstr_tok: ["Update","the","model","with","a","mini-batch","of","features","`","X","`","and","boolean","targets","`","y","`",".","Parameters","--","--","--","--","--","X","A","dataframe","of","features",".","y","A","series","of","boolean","target","values","."]
code: def learn_many(self, X: pd.DataFrame, y: pd.Series):
      """Update the model with a mini-batch of features `X` and boolean targets `y`.
  
      Parameters
      ----------
      X
          A dataframe of features.
      y
          A series of boolean target values.
  
      """
  
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 1 for calling the MiniBatchClassifier obj's underlying member method code with example usage: obj.learn_many(X, y) without return types