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One of the most common metrics used to measure the prediction accuracy of a model is **MSE**, which stands for **mean squared error**. It is calculated as:

**MSE **= (1/n) * Σ(actual – prediction)^{2}

where:

**Σ**– a fancy symbol that means “sum”**n**– sample size**actual**– the actual data value**prediction**– the predicted data value

The lower the value for MSE, the more accurately a model is able to predict values.

**How to Calculate MSE in R**

Depending on what format your data is in, there are two easy methods you can use to calculate the MSE of a regression model in R.

**Method 1: Calculate MSE from Regression Model**

In one scenario, you may have a fitted regression model and would simply like to calculate the MSE of the model. For example, you may have the following regression model:

#load mtcars dataset data(mtcars) #fit regression model model #get model summary model_summ

To calculate the MSE for this model, you can use the following formula:

#calculate MSE mean(model_summ$residuals^2) [1] 8.85917

This tells us that the MSE is **8.85917**.

**Method 2: Calculate MSE from a list of Predicted and Actual Values**

In another scenario, you may simply have a list of predicted and actual values. For example:

#create data frame with a column of actual values and a column of predicted values data #view first six lines of data head(data) pred actual Mazda RX4 23.14809 21.0 Mazda RX4 Wag 23.14809 21.0 Datsun 710 25.14838 22.8 Hornet 4 Drive 20.17416 21.4 Hornet Sportabout 15.46423 18.7 Valiant 21.29978 18.1

In this case, you can use the following formula to calculate the MSE:

#calculate MSE mean((data$actual - data$pred)^2) [1] 8.85917

This tells us that the MSE is **8.85917**, which matches the MSE that we calculated using the previous method.