christian-oleary/emmv
Implementation of EM/MV metrics based on N. Goix et al.
This tool helps data scientists and machine learning engineers evaluate how well their anomaly detection models perform, even when they don't have labeled examples of anomalies. You input your trained anomaly detection model and the dataset it was trained on, and it outputs two scores: Excess Mass and Mass Volume. These scores indicate the model's effectiveness in identifying unusual patterns.
No commits in the last 6 months. Available on PyPI.
Use this if you are a data scientist or ML engineer building anomaly detection systems and need to assess model performance without the luxury of pre-labeled anomaly data.
Not ideal if you already have a perfectly labeled dataset with known anomalies and can use standard classification metrics for evaluation.
Stars
10
Forks
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Language
Python
License
MIT
Category
Last pushed
Sep 20, 2024
Commits (30d)
0
Dependencies
4
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