Hyb Error: a Hybrid Metric Combining Absolute and Relative Errors (2024)

In any case, what I usually want from an error metric is a clear interpretation of what it means, apart from just looking nice. Absolute error is good for measurements where the major error sources are independent of the value, while relative error is good for accuracy loss in floating-point arithmetic (though it gets a bit involved with catastrophic cancellation, where you want to take everything relative to the original input scale). Without a principled reason to do so, I wouldn't want to clump together absolute and relative thresholds and distort their meaning like this.