what is mse

Mean squared error (MSE) is a statistical measure that calculates the average of the squared differences between the estimated values and the actual value. It is a risk function that corresponds to the expected value of the squared error loss. MSE is used to measure the amount of error in statistical models and is a measure of the quality of an estimator. In machine learning, specifically empirical risk minimization, MSE may refer to the empirical risk (the average loss on an observed data set), as an estimate of the true MSE (the true risk: the average loss on the actual population distribution) . The formula for MSE is the average of the squares of the differences between the observed and predicted values. MSE is always a positive value that decreases as the error approaches zero. The MSE is the second moment (about the origin) of the error, and thus incorporates both the variance of the estimator (how widely spread the estimates are from one data sample to another) and its bias (how far off the average estimated value is from the true value) .