MSE (erro médio quadrado)
- Created by
- Renato Passos, Eng. de Software
- Reviewed by
- Renato Passos, Eng. de Software
Last updated: Apr 18, 2026
Formula
MSE
About this calculator
The MSE (mean squared error) calculator is a practical tool for estimating the accuracy of a regression model. It calculates the average of the squared residuals between the actual and predicted values by the model.
The calculation is done using the formula MSE = Σ(yᵢ − ŷᵢ)²/n, where yᵢ are the actual values, ŷᵢ are the predicted values and n is the number of observations. This provides an important measure of the model's quality.
MSE is particularly useful in linear and nonlinear regression scenarios, where model accuracy is critical. Additionally, it can be used to compare the performance of different models and choose the best one for the problem at hand.
Remember that MSE is a mean measure, so it's essential to consider the variability of the residuals to get a more complete view of the model's accuracy.
Frequently asked questions
What is MSE?
MSE (mean squared error) is a measure of regression model accuracy, calculated as the average of the squared residuals between actual and predicted values by the model.
When to use the MSE calculator?
Use the MSE calculator when you need to estimate the accuracy of a linear or nonlinear regression model, or when you need to compare the performance of different models.
What are the precautions when using the MSE calculator?
Remember to consider the variability of the residuals to get a more complete view of the model's accuracy, and use the calculator only to estimate the accuracy of a regression model.