Mean Absolute Error (MAE)

INFO

Measures the average absolute differences between predicted and actual values in regression tasks.

How It Works

$$\text{MAE}=\frac{1}{n}\sum _{i=1}^n\left|y_i-{\hat{y}}_i\right|$$

• $y_i$: Actual value
• ${\hat{y}}_i$: Predicted value
• $n$: Number of data points

MAE gives a straightforward average of prediction errors, treating all deviations equally.

What to Look For

• Lower MAE = better performance
• Easy to interpret
• Less sensitive to outliers than Mean Squared Error (MSE) or Root Mean Squared Error (RMSE)

Application Models

• Deep Neural Network (DNN)
• Recurrent Neural Network (RNN)