class_docstr: Extra Trees Regressor for time series forecasting
  
  Extra Trees Regressor implements a meta estimator that fits a number of
  randomized decision trees (a.k.a. extra-trees) on various sub-samples of
  the dataset and uses averaging to improve the predictive accuracy and
  control over-fitting.
class_docstr_tok: ["Extra","Trees","Regressor","for","time","series","forecasting","Extra","Trees","Regressor","implements","a","meta","estimator","that","fits","a","number","of","randomized","decision","trees","(","a.k.a",".","extra-trees",")","on","various","sub-samples","of","the","dataset","and","uses","averaging","to","improve","the","predictive","accuracy","and","control","over-fitting","."]
code: def __init__(self, config: ExtraTreesForecasterConfig):
      super().__init__(config)
      self.model = ExtraTreesRegressor(
          n_estimators=self.config.n_estimators,
          max_depth=self.config.max_depth,
          min_samples_split=self.config.min_samples_split,
          random_state=self.config.random_state,
      )
  
imports: import logging
  from lightgbm import LGBMRegressor
  from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
  from sklearn.multioutput import MultiOutputRegressor
  from merlion.models.forecast.sklearn_base import SKLearnForecaster, SKLearnForecasterConfig
label_desc: Use image node_id 1 to create a new ExtraTreesForecaster object from inherited base classes: SKLearnForecaster with example: obj = ExtraTreesForecaster(config)