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


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? 

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:
A couple suggestions


MAE represents your mean error. This is essential to recognize as having an error of even half of your MAE on the low end of your spectrum ($34,900) is huge. However, to your point, at the high end of your spectrum it is quite small.
To solve the above, you should plot a graph to identify the patterns in your error. You'll plot your predicted y value vs. your actual y value and see where your predictions deviate from the y=x line.


Normalized metrics:

you may use an r-squared value. This might be ideal because its looking at the percent of variance explained by your regression and thus is a relative measure of fit. Your r-squared value will always be between 0 and 1 (assuming your model is not worse than always guessing your mean value) where 1 is perfect. Implement as follows:

from sklearn.metrics import r2_score
y_pred = model.predict(X_train)
print(r2_score(y_true, y_pred))