MSE stands for Mean Squared Error. And is a measure of how close a fitted line is to data points by measuring the average squared of the errors.
Card 1 of 259. Answer: MSE stands for Mean Squared Error. And is a measure of how close a fitted line is to data points by measuring the average squared of the errors.
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MSE, or mean squared error, is the average of the squared differences between predicted and actual values: MSE = (1/n) Σ (yᵢ − ŷᵢ)². It is the standard loss for regression and the quantity ordinary least squares minimizes.
Squaring does two things. It makes every error positive, so over- and under-predictions don't cancel out, and it punishes large errors much more than small ones: missing by 10 costs 100 times more than missing by 1. That makes MSE sensitive to outliers, which is a feature when big misses really are costly and a problem when the data has noisy extremes.
Because the units are squared (dollars squared, for a price model), people usually report RMSE, the square root of MSE, which is back in the target's units. When outliers should count less, MAE or the Huber loss are the common alternatives.
