Is there any support for KDE (Kernel Density Estimation) based outlier detection planned?
貢獻者指南
技術棧
python
領域
machine learningdata
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-2 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
fresh
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
Pythonoutlier detection basicsnumpy/scipy
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
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.
