class_docstr: Propensity regression model based on the LogisticRegression algorithm.
class_docstr_tok: ["Propensity","regression","model","based","on","the","LogisticRegression","algorithm","."]
code: def _model(self):
      kwargs = {
          "penalty": "elasticnet",
          "solver": "saga",
          "Cs": np.logspace(1e-3, 1 - 1e-3, 4),
          "l1_ratios": np.linspace(1e-3, 1 - 1e-3, 4),
          "cv": StratifiedKFold(
              n_splits=self.model_kwargs.pop("n_fold")
              if "n_fold" in self.model_kwargs
              else 4,
              shuffle=True,
              random_state=self.model_kwargs.get("random_state", 42),
          ),
          "random_state": 42,
      }
      kwargs.update(self.model_kwargs)
  
      return LogisticRegressionCV(**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 1 for calling the LogisticRegressionPropensityModel obj's underlying member method code with example usage: obj._model() and returns: LogisticRegressionCV