  Generalized linear regressions, such as logistic regressions or Poisson
regressions, are long-studied regression analysis approaches, and their
applications are widely employed in various classification problems. Our study
considers a stochastic generalized linear regression model as a stochastic
problem with chance constraints and tackles it using nonconvex programming
techniques. Clustering techniques and quantile estimation are also used to
estimate random data's mean and variance-covariance matrix. Metrics for
measuring the performance of logistic regression are used to assess the model's
efficacy, including the F1 score, precision score, and recall score. The
results of the proposed algorithm were over 1 to 2 percent better than the
ordinary logistic regression model on the same dataset with the above
assessment criteria.
