A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need for post-modeling re-calibration and results in a substantial enhancement of predictive accuracy. Notwithstanding the increased complexity due to the expanded parameter set, we utilize a cyclic coordinate descent optimization to implement the ZIP regression, with adjustments made to address saddle points. We also study how various approaches alleviate the potential drawbacks of incomplete exposures in insurance applications. The procedure is tested on real-life data. We demonstrate a significant improvement in performance relative to other popular alternatives, which justifies our modeling techniques.

中文翻译：

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通过零膨胀泊松回归处理不平衡的保险数据

引入了零膨胀泊松 (ZIP) 回归的机器学习方法，以解决金融数据不平衡引起的常见困难。建议的 ZIP 可以解释为一种自适应权重调整程序，它消除了对建模后重新校准的需要，并大大提高了预测准确性。尽管由于扩展的参数集而增加了复杂性，但我们利用循环坐标下降优化来实现 ZIP 回归，并针对鞍点进行了调整。我们还研究了各种方法如何减轻保险申请中不完整暴露的潜在缺点。该过程在真实数据上进行了测试。与其他流行的替代品相比，我们展示了性能的显着改进，