Mean Absolute Error (MAE)
A regression metric that calculates the average absolute difference between predicted and actual values, giving equal weight to all errors.
Formula
MAE = (1/n) * sum(|predicted - actual|). Easy to interpret: an MAE of 5 means predictions are off by 5 units on average. Less sensitive to outliers than MSE.
When to Use
When all errors matter equally. When outliers should not dominate the metric. For business metrics where the cost of error is proportional to its magnitude.