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 _model(self):
      kwargs = {
          "max_depth": 8,
          "learning_rate": 0.1,
          "n_estimators": 100,
          "objective": "binary:logistic",
          "nthread": -1,
          "colsample_bytree": 0.8,
          "random_state": 42,
      }
      kwargs.update(self.model_kwargs)
  
      return xgb.XGBClassifier(**kwargs)
  
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 2 for calling the GradientBoostedPropensityModel obj's underlying member method code with example usage: obj._model() and returns: xgb