class_docstr: A regressor.
class_docstr_tok: ["A","regressor","."]
docstr: Fits to a set of features `x` and a real-valued target `y`.
  
  Parameters
  ----------
  x
      A dictionary of features.
  y
      A numeric target.
docstr_tok: ["Fits","to","a","set","of","features","`","x","`","and","a","real-valued","target","`","y","`",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","y","A","numeric","target","."]
code: def learn_one(self, x: dict, y: base.typing.RegTarget):
      """Fits to a set of features `x` and a real-valued target `y`.
  
      Parameters
      ----------
      x
          A dictionary of features.
      y
          A numeric target.
  
      """
  
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 1 for calling the Regressor obj's underlying member method code with example usage: obj.learn_one(x, y) without return types