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help wanted
Description
Is there any support for KDE (Kernel Density Estimation) based outlier detection planned?
Contributor guide
- Tech stack
- python
- Domain
- machine learningdata
- Issue type
- feature
- DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
- 3
- Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
- 1-2 days
- Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
- fresh
- ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
- clear
- Prerequisites
- Pythonoutlier detection basicsnumpy/scipy
- Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
- 30
- Research direction
- 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.