class_docstr: Gradient boosted propensity score model with optional early stopping.
  
  Notes
  -----
  Please see the xgboost documentation for more information on gradient boosting tuning parameters:
  https://xgboost.readthedocs.io/en/latest/python/python_api.html
class_docstr_tok: ["Gradient","boosted","propensity","score","model","with","optional","early","stopping",".","Notes","--","--","-","Please","see","the","xgboost","documentation","for","more","information","on","gradient","boosting","tuning","parameters",":","https",":","\/\/xgboost.readthedocs.io\/en\/latest\/python\/python_api.html"]
code: def __init__(
      self,
      early_stop=False,
      clip_bounds=(1e-3, 1 - 1e-3),
      **model_kwargs,
  ):
      super(GradientBoostedPropensityModel, self).__init__(
          clip_bounds, **model_kwargs
      )
      self.early_stop = early_stop
  
imports: from abc import ABCMeta, abstractmethod
  import logging
  import numpy
  from pygam import LogisticGAM, s
  from sklearn.metrics import roc_auc_score
  from sklearn.linear_model import LogisticRegressionCV
  from sklearn.model_selection import StratifiedKFold, train_test_split
  import xgboost
label_desc: Use image node_id 1 to create a new GradientBoostedPropensityModel object from inherited base classes: PropensityModel with example: obj = GradientBoostedPropensityModel(early_stop, clip_bounds)