为有效管理泥浆流失这一钻井工程中的挑战，针对伊朗马伦油田的不同泥浆流失严重程度，构建了4个平衡数据集并进行了分类研究。通过采用不同交叉验证策略并以多个平均性能指标作为优化目标，寻找到了多个模型的最优超参数，并最终确定了最佳模型及其参数配置。结果表明，所得到的最佳模型在精度、召回率和F₁分数等方面都有显著提升。特别的，对于不同的井漏程度数据集使用不同的模型（如极致梯度提升树和类别型梯度提升树），结果中各项指标都处于0.95以上，显示了模型的出色性能。此外，通过计算沙普利解释值对最优模型进行解释，进一步深入理解了预测行为。可见，超参数优化对于增强模型性能有重大影响，而模型解释对于理解预测机制是必要的。

To ensure an effective management of circulation loss, a critical challenge within drilling operations, four balanced datasets was developed aimed at categorizing varying severity levels of circulation loss at Iran's Marun Oil Field. Employing diverse cross-validation strategies and setting multiple average performance metrics as the optimization objectives, optimal hyperparameters was identified across several models, subsequently determining the best model and its parameter setup. It was found that the selected optimal model exhibited significant enhancements in accuracy, recall, and F₁-scores. Specifically, different models, such as extreme gradient boosting and categorical gradient boosting, were applied to datasets corresponding to various well leakage degrees, with all performance metrics surpassing the 0.95 threshold, thereby illustrating the models' superior performance. Moreover, by computing shapley Additive explanations values, the study provided deeper insights into the predictive behaviors, underscoring the substantial influence of hyperparameter optimization on model performance enhancement. Additionally, model interpretation emerged as crucial for a profound understanding of the predictive mechanisms, highlighting the necessity of employing such techniques in contemporary research to elucidate model functionalities and outcomes.