statsmodels.tools.eval_measures.mse¶
-
statsmodels.tools.eval_measures.
mse
(x1, x2, axis=0)[source]¶ mean squared error
- Parameters
- x1, x2array_like
The performance measure depends on the difference between these two arrays.
- axisint
axis along which the summary statistic is calculated
- Returns
- msendarray or float
mean squared error along given axis.
Notes
If
x1
andx2
have different shapes, then they need to broadcast. This usesnumpy.asanyarray
to convert the input. Whether this is the desired result or not depends on the array subclass, for example numpy matrices will silently produce an incorrect result.