aiml-pyod-detection
CommunityDetect anomalies in text embeddings with PyOD.
Authorwuyoscar
Version1.0.0
Installs0
System Documentation
What problem does it solve?
ISC PyOD detection template provides a guided workflow to validate anomaly detection on text embeddings by using IsolationForest to distinguish normal samples from outliers in an AI safety evaluation dataset.
Core Features & Use Cases
- Deterministic anomaly detection: employs PyOD's IForest on textual embeddings to flag anomalous samples.
- Polarity-based verification: includes a semantic polarity mechanism to ensure outliers differ from normal samples.
- Reproducible evaluation template: provides script scaffolding and sample prompts for consistent testing.
Quick Start
Run the anomaly-detection template on a sample dataset to validate the PyOD integration.
Dependency Matrix
Required Modules
pyodsentence-transformersnumpy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: aiml-pyod-detection Download link: https://github.com/wuyoscar/ISC-Bench/archive/main.zip#aiml-pyod-detection Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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