Calculate MAE for Regression Model
In this method, we are using the regression model instead of the vector used in the previous examples as with the definition to the MAE said that it is used to get the accuracy of the built model, so in the approach, we are simply creating a dataframe of 10 columns and 3 rows containing integers value and with it, we are simply using the mae() function passed with the respected parameters of the columns of the built data frame to get the MAE of the model.
In this example, we are have created a dataframe of 3 rows and 10 columns and then further fitted this data frame to a linear regression model at the last, we are calculating the model accuracy MAE using the predicted value of the model vs actual values of it in R.
R
# Import required package library(Metrics) # Create Data frame gfg_df <- data.frame(x1=c(4,8,9,4,1,8,9,4,1,6), x2=c(1,3,2,8,9,9,6,7,4,1), y=c(8,5,1,3,2,4,1,6,2,5)) # fit regression model model <- lm(y~x1+x2, data=gfg_df) # display mae mae(gfg_df$y, predict(model)) |
Output:
[1] 1.782963
How to Calculate MAE in R
In this article, we are calculating the Mean Absolute Error in the R programming language.
Mean Absolute Error: It is the measure of errors between paired observations expressing the same phenomenon and the way to measure the accuracy of a given model. The formula to it is as follows:
MAE = (1/n) * Σ|yi – xi|
Where,
- Σ: Sum
- yi: Observed value for ith observation
- xi: Predicted value for ith observation
- n: Total number of observations
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