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.

引入了一种用于零膨胀泊松（ZIP）回归的机器学习方法，以解决由于财务数据不平衡而引起的常见困难。可以将建议的ZIP解释为一种自适应权重调整程序，该程序无需进行建模后重新校准，从而可以大大提高预测准确性。尽管由于扩展了参数集而导致复杂性增加，但我们仍使用循环坐标下降优化来实现ZIP回归，并进行了调整以解决鞍点。我们还研究了各种方法如何减轻保险申请中不完全敞口的潜在弊端。该程序已根据实际数据进行了测试。与其他流行的替代产品相比，我们证明了性能的显着提高，