class_docstr: Sample from the FriedmanGSG generator.
  
  This sample contains 10k instances sampled from the Friedman generator and presents
  global and slow gradual concept drifts that affect the data and happen after
  3.5k and 7k instances. The transition window between different concepts has a length of
  1k instances.
class_docstr_tok: ["Sample","from","the","FriedmanGSG","generator",".","This","sample","contains","10k","instances","sampled","from","the","Friedman","generator","and","presents","global","and","slow","gradual","concept","drifts","that","affect","the","data","and","happen","after","3.5k","and","7k","instances",".","The","transition","window","between","different","concepts","has","a","length","of","1k","instances","."]
code: def __init__(self):
      super().__init__(
          n_samples=10000,
          n_features=10,
          task=datasets.base.REG,
          gen=datasets.synth.FriedmanDrift(
              drift_type="gsg",
              position=(3500, 7000),
              transition_window=1000,
              seed=42,
          ),
      )
  
file_path: river/river/evaluate/gen.py
imports: from __future__ import annotations
  from river import datasets
label_desc: Use image node_id 1 to create a new FriedmanGSG10k object from inherited base classes: WrappedGenerator with example: obj = FriedmanGSG10k()