Understanding to MSE

Data Science, AI and ML
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#1

What is MSE?

#2

Mean squared error MSE or mean squared deviation is the measure of the average of the squares of the errors, that is, the average squared difference between the estimated values and the actual value.

#3

MSE is the mean of the squared difference between your estimate and the data. This is an identical calculation to the calculation of the variance of a statistic, where the estimate is the mean. Smaller MSE generally indicates a better estimate, at the data points in question.

One key piece to remember, when the estimator results from a regression or machine learning calculation, very small MSE may mean that the data is being overfit by your estimator. Overtraining, too many degrees of freedom for the data or an under-constrained fit might be difficult to identify unless you have well thought out, a priori methods to test and validate your system.