July 2016
Beginner to intermediate
462 pages
9h 14m
English
Content preview from Python Data Analysis Cookbook
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Computing MSE and median absolute error
The mean squared error (MSE) and median absolute error (MedAE) are popular regression metrics. They are given by the following equations:
The MSE (10.6) is analogous to population variance. The square root of the MSE (RMSE) is, therefore, analogous to standard deviation. The units of the MSE are the same as the variable under analysis—in our case, temperature. An ideal fit has zero-valued residuals and, therefore, its MSE is equal to zero. Since we are dealing with squared errors, the MSE has values that are larger or ideally equal to zero.
The MedAE is similar to the MSE, but we start with the absolute values ...
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