# Normalized metric for comparing regression models performance

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

1. MAE=17,000 is just 2.5% of the difference between max and min
2. 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? Which of those metrics are available in scikit-learn? For instance, it provides mean_absolute_error and mean_squared_error but they are not normalized. Edit: 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: • Could you elaborate on what you mean by whether or not switching from one model to another might be "significant"? – Upper_Case May 24 '19 at 18:13 • @Upper_Case By switching from one model to another I mean for instance: 1) switching from decision tree and random forest or 2) changing data preprocessing approach: switching feature drop to imputing with mean value. Defining what significant mean is actually core part of this question. If you don't know the context, knowing that MAE has dropped by$225 won't tell you much. It would, however, tell you more, if it was normalized, i.e, max possible MAX was 1.0, minimal -- 0.0. Then seeing drop by 0.1 would be significant to me. – dzieciou May 27 '19 at 15:18