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Generalized linear regressions, such as logistic regressions or Poissonregressions, are long-studied regression analysis approaches, and theirapplications are widely employed in various classification problems. Our studyconsiders a stochastic generalized linear regression model as a stochasticproblem with chance constraints and tackles it using nonconvex programmingtechniques. Clustering techniques and quantile estimation are also used toestimate random data's mean and variance-covariance matrix. Metrics formeasuring the performance of logistic regression are used to assess the model'sefficacy, including the F1 score, precision score, and recall score. Theresults of the proposed algorithm were over 1 to 2 percent better than theordinary logistic regression model on the same dataset with the aboveassessment criteria.

A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming