I was recently trying to explain to someone whether performance of my estimation approach is good or bad. For instance, whether a model with Mean Absolute Error (MAE) of 17000 is a bad solution. It was also hard for me to explain whether performance loss by 225 (in terms of MAE), when switching from one model to another, is significant or not.
To me it was clear that both are little because I knew the context: we we're talking about predicting house prices ranging from \$34,900 (min) to \$755,000 (max), so that
- MAE=17,000 is just 2.5% of the difference between max and min
- Change in MAE by \$225 is just 0.03% of the difference between max and min.
Are there some normalized metrics for comparing performance of regression models without the need to know the context?
After Joe B suggestions, I've plotted a graph to see deviations between predicted and expected price. In fact, that's gives more insight than a single-number metric: