class_docstr: Sample from the FriedmanLEA generator.
  
  This sample contains 10k instances sampled from the Friedman generator and presents
  local-expanding abrupt concept drifts that locally affect the data and happen after
  2k, 5k, and 8k instances.
class_docstr_tok: ["Sample","from","the","FriedmanLEA","generator",".","This","sample","contains","10k","instances","sampled","from","the","Friedman","generator","and","presents","local-expanding","abrupt","concept","drifts","that","locally","affect","the","data","and","happen","after","2k",",","5k",",","and","8k","instances","."]
code: def __init__(self):
      super().__init__(
          n_samples=10000,
          n_features=10,
          task=datasets.base.REG,
          gen=datasets.synth.FriedmanDrift(
              drift_type="lea", position=(2000, 5000, 8000), 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 FriedmanLEA10k object from inherited base classes: WrappedGenerator with example: obj = FriedmanLEA10k()