MathLiveMean squared error
Gives the average of the squared differences between estimated values and actual observed values. It is used to measure how accurate a prediction, estimator, or regression model is, with smaller values meaning better fit.
\operatorname{MSE}=\frac{1}{n}\sum_{i=1}^{n}(\hat{Y}_i-Y_i)^2What the symbols mean
| number of observations or data points | |
| index running from 1 to n over the data points | |
| estimated or predicted value for the i-th observation | |
| actual measured value of the i-th observation | |
| mean squared error |
About this formula
- FieldStatistics
- TopicEstimation and model evaluation
- LevelUniversity
- Also calledMSE formula, mean squared error formula, average squared error, mean squared deviation, MSE estimator error, forecast error squared average
- SourceWikidata Q1940696
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