用贝叶斯方法对比逻辑回归与概率回归，提升银行理财订阅预测准确率。
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns
- 采用贝叶斯方法与留一法交叉验证评估模型性能。
- 逻辑回归在预测银行定期存款订阅上表现优于概率回归。
- 适合关注金融营销建模与不平衡数据处理的从业者。
在竞争激烈的银行业环境中，精准预测客户行为对提升营销效果和财务收益至关重要。本研究基于葡萄牙银行的直接营销数据，评估逻辑回归（logit）与概率回归（probit）模型在预测定期存款订阅方面的有效性。数据包含多种人口统计、经济及行为特征，影响订阅概率。为克服数据固有的不平衡性，对目标变量进行了平衡处理。通过贝叶斯方法与留一法交叉验证（LOO-CV）评估模型预测能力。结果表明，逻辑回归在该分类任务中表现更优。研究强调了在金融服务业复杂决策与不平衡数据场景下模型选择的重要性，为银行优化决策流程、改进客户细分和提升营销策略提供了依据。
原文摘要 · Abstract (English)
In the highly competitive environment of the banking industry, it is essential to precisely forecast the behavior of customers in order to maximize the effectiveness of marketing initiatives and improve financial consequences. The purpose of this research is to examine the efficacy of logit and probit models in predicting term deposit subscriptions using a Portuguese bank's direct marketing data. There are several demographic, economic, and behavioral characteristics in the dataset that affect the probability of subscribing. To increase model performance and provide an unbiased evaluation, the target variable was balanced, considering the inherent imbalance in the dataset. The two model's prediction abilities were evaluated using Bayesian techniques and Leave-One-Out Cross-Validation (LOO-CV). The logit model performed better than the probit model in handling this classification problem. The results highlight the relevance of model selection when dealing with complicated decision-making processes in the financial services industry and imbalanced datasets. Findings from this study shed light on how banks can optimize their decision-making processes, improve their client segmentation, and boost their marketing campaigns by utilizing machine learning models.
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