Error rate difference evaluation metric
Last updated: Feb 13, 2025
The error rate difference metric measures the percentage of transactions that are incorrectly scored by your model.
Metric details
Error rate difference is a fairness evaluation metric that can help determine whether your asset produces biased outcomes.
Scope
The error rate difference metric evaluates generative AI assets and machine learning models.
- Types of AI assets:
- Prompt templates
- Machine learning models
- Generative AI tasks: Text classification
- Machine learning problem type: Binary classification
Scores and values
The error rate difference metric score indicates the difference in error rate for the monitored and reference groups.
- Range of values: 0.0-1.0
- Ratios:
- At 0: Both groups have equal odds
Do the math
The following formula is used for calculating the error rate (ER):
The following formula is used for calculating the error rate difference:
Parent topic: Evaluation metrics
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