class_docstr: An estimator.
class_docstr_tok: ["An","estimator","."]
docstr: Indicates whether or not the estimator is supervised or not.
  
  This is useful internally for determining if an estimator expects to be provided with a `y`
  value in it's `learn_one` method. For instance we use this in a pipeline to know whether or
  not we should pass `y` to an estimator or not.
docstr_tok: ["Indicates","whether","or","not","the","estimator","is","supervised","or","not",".","This","is","useful","internally","for","determining","if","an","estimator","expects","to","be","provided","with","a","`","y","`","value","in","it","'s","`","learn_one","`","method",".","For","instance","we","use","this","in","a","pipeline","to","know","whether","or","not","we","should","pass","`","y","`","to","an","estimator","or","not","."]
code: def _supervised(self):
      """Indicates whether or not the estimator is supervised or not.
  
      This is useful internally for determining if an estimator expects to be provided with a `y`
      value in it's `learn_one` method. For instance we use this in a pipeline to know whether or
      not we should pass `y` to an estimator or not.
  
      """
      return True
  
file_path: river/river/base/estimator.py
imports: from __future__ import annotations
  import abc
  from .None import base
label_desc: Use image node_id 1 for calling the Estimator obj's underlying member method code with example usage: obj._supervised() and returns: True