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.

翻译：广义线性回归（如逻辑回归或泊松回归）是长期研究的回归分析方法，其应用广泛涉及多种分类问题。本研究将随机广义线性回归模型视为含机会约束的随机问题，并采用非凸规划技术进行求解。同时，利用聚类技术和分位数估计来估计随机数据的均值与方差-协方差矩阵。采用逻辑回归性能度量指标（包括F1分数、精确率和召回率）评估模型效能。在相同数据集上，所提算法在上述评估指标上的结果比普通逻辑回归模型提升1%至2%。