I want to evaluate the performance of my model, but the problem I have is I have always used a confusion matrix because I have always done models with categorical output (classification). Now, I have this model with numerical output and I find neither a way nor explanation of how to evaluate his performance, and when I use other kernels codes they give me the % accuracy (if it is the accuracy?) and I can't find any references or infer how this % is computed.
So, with a model with output as numerical, how and where can I find techniques of evaluation? (and their explanation, because I don't like to use things I don't understand/know).
I'm working with python.
The most popular techniques used to evaluate regression models that come to my mind are:
Mean Square Error (and all it's possible variations e.g. Mean Absolute Error, Mean Absolute Percentage Error, Mean Percentage Error)
If you are interested in how to calculate percentage error you probably want to look at sections "Mean absolute percentage error" and "Mean percentage error" in the article I mentioned above.