code: def __init__(
      self,
      max_depth=3,
      learning_rate=0.1,
      n_estimators=100,
      silent=True,
      objective="binary:logistic",
      booster="gbtree",
      n_jobs=-1,
      gamma=0,
      min_child_weight=1,
      max_delta_step=0,
      subsample=1,
      colsample_bytree=1,
      colsample_bylevel=1,
      reg_alpha=0,
      reg_lambda=1,
      scale_pos_weight=1,
      base_score=0.5,
      random_state=0,
      missing=None,
      eval_metric="logloss",
      scoring_metric="auc",
      **kwargs,
  ):
      kwargs = locals()
      kwargs.pop("self")
      kwargs.pop("__class__", None)
      kwargs = dict(kwargs, **(kwargs.pop("kwargs", {})))
      if "random_state" not in kwargs and "seed" in kwargs:
          kwargs["random_state"] = kwargs.pop("seed")
      if "n_jobs" not in kwargs and "nthread" in kwargs:
          kwargs["n_jobs"] = kwargs.pop("nthread")
      super(BinaryClassifier, self).__init__(**kwargs)
  
imports: from __future__ import absolute_import
  import inspect
  import logging
  import warnings
  import threading
  import lore.env
  import lore.estimators
  from lore.util import timed, before_after_callbacks
  import xgboost
label_desc: Use image node_id 1 to create a new BinaryClassifier object from inherited base classes: Base, xgboost with example: obj = BinaryClassifier(max_depth, learning_rate, n_estimators, silent, objective, booster, n_jobs, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree, colsample_bylevel, reg_alpha, reg_lambda, scale_pos_weight, base_score, random_state, missing, eval_metric, scoring_metric)