metric_mse(a, b, axis=None)¶
Computes the mean squared error (MSE) or mean squared deviation (MSD) between given array_like \(a\) and \(b\) variables.
a (array_like) – \(a\) variable.
b (array_like) – \(b\) variable.
axis (None or int or tuple of ints, optional) – Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
Mean squared error (MSE).
- Return type
>>> a = np.array([0.48222001, 0.31654775, 0.22070353]) >>> b = a * 0.9 >>> metric_mse(a, b) 0.0012714...