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How to interpret evaluation outputs
Use this guide after batdetect2 evaluate has written metrics and plots to disk.
Start by identifying the task
Do not interpret a metric until you know which evaluation task produced it.
For example, a detection score and a clip-classification score answer different questions.
Read the output directory as a bundle
Treat the evaluation output directory as one package:
- metrics,
- plots,
- saved predictions,
- config context.
Do not lift a single number out of context and treat it as the whole story.
Look for failure patterns, not just overall averages
Check:
- whether errors concentrate in certain taxa,
- whether specific sites or recorder setups behave differently,
- whether threshold choices are driving the result,
- whether predictions are near clip boundaries or matching thresholds.
Keep validation and deployment questions separate
A model can look good on one task and still be a poor fit for your deployment question.
Interpret the outputs in relation to the real use case, not only the easiest metric to report.
Related pages
- Evaluation tutorial: {doc}
../tutorials/evaluate-on-a-test-set - Evaluation concepts: {doc}
../explanation/evaluation-concepts-and-matching - Model output and validation: {doc}
../explanation/model-output-and-validation