class_docstr: This predictor selects between the target mean and the the user-selected regression model
   automatically. Faded error metrics are kept for both the predictors, and the most
  accurate one is selected automatically for each incoming instance.
class_docstr_tok: ["This","predictor","selects","between","the","target","mean","and","the","the","user-selected","regression","model","automatically",".","Faded","error","metrics","are","kept","for","both","the","predictors",",","and","the","most","accurate","one","is","selected","automatically","for","each","incoming","instance","."]
code: def predict_one(self, x: dict):
      if self._mae_mean <= self._mae_model:
          return self.mean_predictor.predict_one(x)
      else:
          return self.model_predictor.predict_one(x)
  
imports: from __future__ import annotations
  import collections
  import functools
  import io
  import math
  import typing
  from river import anomaly, base, drift, linear_model, stats
  from river.tree import split_criterion
  from river.tree import splitter
  from .base import HoeffdingRule
label_desc: Use image node_id 3 for calling the AdaptiveRegressor obj's underlying member method code with example usage: obj.predict_one(x) and returns: self, self