Rule of thumb for good number of features when dealing with grouped data I have a classification problem on clinical data where I have multiple samples for each patient. So the samples related to the same patient are somehow dependent from each other.

I know that is not possible to know a priori the optimal number of features to use, but there are some rule of thumb that works in many cases.

My question is: are those rules valid also in my case? In particular, I should relate the number of features to the number of instances or to the number of groups?

Thanks
This a really hard question to answer. I recommend you do some reading to get a feeling on what can be done and particularly, what can be done for your particular task.

This paper is a must. But if you prefer a more practical approach have a look at these two interesting sources:

a) ML Mastery which also provides additional further readings

b) Kaggle

Good luck!