Is there any support for KDE (Kernel Density Estimation) based outlier detection planned?
贡献者指南
技术栈
python
领域
machine learningdata
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
fresh
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
Pythonoutlier detection basicsnumpy/scipy
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
30
研究方向
The issue requests adding Kernel Density Estimation (KDE) based outlier detection to pyod. To implement, first review existing KDE implementations in scikit learn (sklearn.neighbors.KernelDensity) and understand how outlier scores can be derived from density estimates. Check the pyod base class structure and existing detectors like ABOD or LOF for integration patterns. Since no additional details are provided, consider discussing the desired interface in the issue comments to align with maintainer expectations.
