  Suppose $x$ is an approximation of $y$. This paper proposes using
$\frac{|x-y|}{1+|y|}$, named Hyb Error, to measure the error. This metric
equals half the harmonic mean of absolute error and relative error, effectively
combining their advantages while mitigating their limitations. For example, Hyb
Error approaches absolute error as $|y|$ approaches 0, thereby avoiding the
exaggeration of relative error, and approaches relative error as $|y|$
approaches infinity, thereby avoiding the exaggeration of absolute error. The
Hyb Error of $\epsilon$ is equivalent to $|x-y|=\epsilon+\epsilon |y|$, which
implies $\mathrm{isclose}(x,y,\epsilon,\epsilon)=\mathrm{True}$, where
``isclose'' is a common floating-point equality check function in numerical
libraries. For sequences, this property makes the Maximum Element-wise Hyb
Error (MEHE) a pragmatic error metric that reflects the most significant error
and equals the decision boundary of the ``isclose'' function.
