# How to get an intuitive value for regression module evaluation?

For regression module evaluation, I think only the MAE (Mean absolute error) value is not objective or practical.
Consider following situations:

• A
MAE=1 while target value follows the uniform distribution on the interval [1,100]
• B
MAE=1 while target value follows the uniform distribution on the interval [1,10]

Obviously A model is better.

So how to get an intuitive value for regression module evaluation, regardless the data set's target value's scope?

• You described a metric of model precision to us. What kind of answer do you want? Nov 18, 2017 at 8:19
• "Obviously B model is better" - not obvious to me at all. If I rescale the interval [1,100] to [1,10], the MAE is only approximately 0.1. I can't quickly see if comparing models on different datasets is meaningful. To me, for a fixed dataset, R^2 is just a way to see how much better a predictor is than the simple average. Similar for your value, except that for MAE the mean is not so natural mathematically, perhaps even worse than median. Nov 18, 2017 at 9:39
• @DavidDale , To get a intuitive value, indicating how many times the model better than another model which always predicts mean value. Nov 20, 2017 at 2:22
• @Valentas . Sorry, I was careless. I edit my post and give my own answer. Please make an remark. Nov 20, 2017 at 2:32
• have you tried using MAPE instead of MAE? Nov 20, 2017 at 2:42

Refer to $$R^2=1-\frac{\sum ( y_i - \hat y_i)^2} {\sum (y_i - \bar y)^2}$$
My method is: $$My Value=\frac {\sum |y_i - \bar y|} {\sum | y_i - \hat y_i|}$$ Indicating how many times the model better than other model, always predicts mean value.