Airbnb 面试真题解析：欺诈预订模型的缺失值填补策略

We’ve provided a labeled training set for a fraudulent booking model.

Each record is a historical Airbnb booking and its corresponding characteristics (or features). These features are:

• price: dollar amount paid for the reservation
• nights: number of nights
• market_avg_price_per_night: average price per night paid in the market
• past_delta_checkin: days between past reservations by the guest
• listing_market: market of the listing
• host_past_nights: number of prior nights hosted by the host
• ds: date-stamp of the reservation
• label: whether the reservation is fake (1) or not (0)
• pred_score: risk score from a model trained on this data set

Question:

Explore the data set and propose a good imputation strategy for missing values.

这道 Airbnb 数据处理题的核心是：先分析各字段的缺失模式，再针对不同类型特征制定有区分度的填补方案。像数值型字段可以按市场、日期或分组统计进行中位数 / 分位数填补，并结合缺失指示器保留“是否缺失”这一信号；类别型字段可用众数、专属缺失类别或按市场分层填补；对于时间相关特征，还要注意避免未来信息泄露。若缺失本身与欺诈风险相关，建议把“缺失”当作一种特征来源，而不是简单删除，从而提升模型对异常预订的能力。