class_docstr: Configuration class for bagging tree-based forecaster model.
class_docstr_tok: ["Configuration","class","for","bagging","tree-based","forecaster","model","."]
docstr: :param n_estimators: number of base estimators for the tree ensemble
  :param max_depth: max depth of base estimators
  :param random_state: random seed for bagging
docstr_tok: [":","param","n_estimators",":","number","of","base","estimators","for","the","tree","ensemble",":","param","max_depth",":","max","depth","of","base","estimators",":","param","random_state",":","random","seed","for","bagging"]
code: def __init__(
      self,
      n_estimators: int = 100,
      max_depth: int = None,
      random_state: int = None,
      **kwargs,
  ):
      """
      :param n_estimators: number of base estimators for the tree ensemble
      :param max_depth: max depth of base estimators
      :param random_state: random seed for bagging
      """
      super().__init__(**kwargs)
      self.n_estimators = n_estimators
      self.random_state = random_state
      self.max_depth = max_depth
  
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 _TreeEnsembleForecasterConfig object from inherited base classes: SKLearnForecasterConfig with example: obj = _TreeEnsembleForecasterConfig(n_estimators, max_depth, random_state)