mean squared error

Average of the squares of the errors between estimated and actual values

Entitymeasure of forecasting qualityQ1940696

Also known as MSE

Instance of: measure of forecasting quality

Overview

From Wikipedia, the free encyclopedia · Read full article · Text CC BY-SA 4.0

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive is because of randomness or because the estimator does not account for information that could produce a more accurate estimate. In machine learning, specifically empirical risk minimization, MSE may refer to the empirical risk, as an estimate of the true MSE.

Overview

All data

11 statements · 18% with external sources · numbers in brackets are citations

Defining formula: \operatorname{MSE}=\frac{1}{n}\sum_{i=1}^n(\hat{Y_i} - Y_i)^2
Short name: MSE
PKN
Different from: root-mean-square deviation
In defining formula: n symbol represents: number of entities
\hat{Y_i} symbol represents: estimate
Y_i symbol represents: measured quantity value

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4 identifiers linking mean squared error across the web's reference databases

Data quality

statements

18%

externally sourced

Source last edited September 21, 2025. AIDB refreshes this page daily.

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AIDB. (2026). mean squared error (Q1940696). Retrieved from https://aidb.si/e/mean-squared-error-Q1940696