## Mean Absolute Error | Calculate MAE using R Studio

The Mean Absolute Error (MAE) measures the average magnitude of the errors in a set of predictions, without considering their direction. The MAE (Mean Absolute Error) is the average over the test sample of the absolute differences between predicted value and observed value.

MAE (Mean absolute error) represents the difference between the observed and predicted values. extracted by averaged the absolute difference over the data set.

Mean Absolute Error (MAE) is the mean of the absolute errors. The absolute error is the absolute value of the difference between the predicted value and the observed value.

The MAE (Mean Absolute Error) measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables.

**Application**

+ In this example we will use the data of three climate stations. You can add other stations in the script.

+ To begin, you need to add your data in R studio.

+ And follow the video below.

+ Install packages: MLmetrics

+ mae(Obs,Sim)

Obs : observed values

Sim : simulated values

**Example :**mae (data_source$simulated , data_source$observed )

Create and save your R Script

**MAE**

mae.s1<- mae (data_source$simulated , data_source$observed )

mae.s1

mae.s2<- mae (data_source$simulated , data_source$observed)

mae.s2

mae.s1

mae.s2<- mae (data_source$simulated , data_source$observed)

mae.s2

mae.s3<- mae (data source$simulated , data source$observed)

mae.s3

mae.s3