I'm working in a regression problem, related to bio-signals, where my labels are integer numbers between 0 and 10. I've tried a couple of regression algorithms already, mainly linear regression.
Edit: More precisely my dataset is composed by 105 rows, and I have 40 features (real numbers). The output Y has 10 ordered values (0, 1, 2, ... 10) which represent the level of anxiety (it's the answer of a questionnaire).
The output are in on a interval scale and are not just ordinal.
Due to the nature of the problem, me and my collegues have considered the possibility of reducing the output, from a range of 10 values to 3 different output. Something like 1, 2, 3 or for example "low", "medium", "high".
Edit: Basically I want to reduce the size of the interval of the output.
My questions are:
- Once the discretization (reduction of number of labels) has been performed, should we still use regression algorithms, or since the low number of output we are in a classification scenario?
- Which is the best way to reduce the number of labels?
- Would it have sense to continue using regression and then categorize the prediction output in three different levels?