class_docstr: A recipe using SkOpt search algorithm for XGBoost Regressor.
docstr: Constructor.
  
  :param num_rand_samples: number of hyper-param configurations sampled
    randomly
  :param n_estimators_range: range of number of gradient boosted trees.
  :param max_depth_range: range of max tree depth
  :param lr: learning rate
  :param min_child_weight: minimum sum of instance weight(hessian)
    needed in a child.
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code: def __init__(
      self,
      num_rand_samples=10,
      n_estimators_range=(50, 1000),
      max_depth_range=(2, 15),
      lr=(1e-4, 1e-1),
      min_child_weight=[1, 2, 3],
  ):
      """
      Constructor.
  
      :param num_rand_samples: number of hyper-param configurations sampled
        randomly
      :param n_estimators_range: range of number of gradient boosted trees.
      :param max_depth_range: range of max tree depth
      :param lr: learning rate
      :param min_child_weight: minimum sum of instance weight(hessian)
        needed in a child.
      """
      super(self.__class__, self).__init__()
  
      self.num_samples = num_rand_samples
  
      self.n_estimators_range = n_estimators_range
      self.max_depth_range = max_depth_range
      self.lr = hp.loguniform(lr[0], lr[1])
      self.min_child_weight = hp.choice(min_child_weight)
  
imports: from bigdl.chronos.autots.deprecated.config.base import Recipe
  from bigdl.orca.automl import hp
  from bigdl.chronos.utils import deprecated
label_desc: Use image node_id 1 to create a new XgbRegressorSkOptRecipe object from inherited base classes: Recipe with example: obj = XgbRegressorSkOptRecipe(num_rand_samples, n_estimators_range, max_depth_range, lr, min_child_weight)