Metrics for unsupervised anomaly detection models
Project description
EMMV
Implementation of EM/MV metrics based on N. Goix et al.
This is a means of evaluating anomaly detection models without anomaly labels
Installation
pip install emmv
Example Use
from emmv import emmv_scores
test_scores = emmv_scores(model, features)
- Where 'model' is your trained scikit-learn model
- Where 'features' is a 2D dataframe of features (the X matrix)
Example resulting object:
{
"em": 0.77586,
"mv": 0.25367
}
Interpreting scores
- The best model should have the highest Excess Mass score
- The best model should have the lowest Mass Volume score
- Probably easiest to just use one of the metrics
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