Class Imbalance Evaluation Protocol
The Clarus benchmark includes datasets with varying class distributions.
These datasets test whether models rely on prevalence rather than stability reasoning.
Imbalance Regimes
Balanced
50% stable
50% unstable
Mild Imbalance
70% stable
30% unstable
Severe Imbalance
90% stable
10% unstable
Extreme Imbalance
99% stable
1% unstable
Purpose
These datasets evaluate model robustness when instability events are rare.
This reflects real-world systems where collapse events are infrequent.
Evaluation
The prediction task remains unchanged.
Performance should be evaluated using:
- precision
- recall
- F1 score
Accuracy alone is insufficient for highly imbalanced datasets.