microsoft/FLAML

support anomaly detection

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#413 opened on Jan 19, 2022

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enhancementgood first issue

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Description

Add a task type 'anomaly_detection', and at least one learner for it.

From my understanding there are multiple types of anomalies - point anomalies, contextual anomalies, and collective anomalies - and also different types of anomaly detection algorithms - nearest-neighbor, clustering, classification, and statistic based.

If anyone has any suggestions or requests for this task, please comment.

Contributor guide

Research direction
Investigate the existing FLAML task types, particularly how they are structured and registered. Then, design and implement an 'anomaly detection' task type with at least one learner, e.g., using isolation forest or k nearest neighbors. Start by reading the developer guide and examining examples of other task types.
Tech stack
python
Domain
backend
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.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonMachine LearningGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
55