How to Calculate Mean Absolute Percentage Error (MAPE) in R
How to Calculate MAPE in R, when want to measure the forecasting accuracy of a model the solution is MAPE.
MAPE stands for mean absolute percentage error.
The mathematical formula to calculate MAPE is:
MAPE = (1/n) * Σ(|Original – Predicted| / |Original|) * 100
where:
Σ –indicates the “sum”
n – indicates the sample size
actual – indicates the actual data value
forecast – indicates the forecasted data value
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Why MAPE?
MAPE is one of the easiest methods and easy to infer and explain. Suppose MAPE value of a particular model is 5% indicate that the average difference between the predicted value and the original value is 5%.
In this tutorial, we are going to cover two different approaches used to calculate MAPE in R.
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Approach 1: Function
Let’s create a data frame with actual and predicted values.
create a dataset
data <- data.frame(actual=c(44, 47, 34, 47, 58, 48, 46, 53, 32, 37, 26, 24), forecast=c(44, 40, 46, 43, 46, 58, 45, 44, 53, 30, 32, 23))
View the dataset
data
actual forecast 1 44 44 2 47 40 3 34 46 4 47 43 5 58 46 6 48 58 7 46 45 8 53 44 9 32 53 10 37 30 11 26 32 12 24 23
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Now we can calculate MAPE in R based on our own function.
We can make use of the following function for MAPE calculation.
mean(abs((data$actual-data$forecast)/data$actual)) * 100 [1] 19.26366
For the current model, the MAPE value is 19.26, It’s indicated that the average absolute difference between the predicted value and the original value is 19.26%.
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Approach 2: Based on Package
The in-built function is available from MLmetrics package. Let’s make use of the same.
The syntax for MAPE calculation is
MAPE(y_pred, y_true)
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where:
y_pred: predicted values
y_true: original values
Let’s load the library
library(MLmetrics)
calculate MAPE
MAPE(data$forecast, data$actual) [1] 0.1926366
Now you can see, exactly the same value we got from our own function from the earlier approach.
