The group that I work with sent me 10 samples, 5 controls and 5 treatment. They asked me to try to use machine learning in this dataset, to see if it could separate the two groups.
I believe that this amount of data is very very low, and I'm quite sure that I can't get any reliable result from this. But, as a beginner in Machine Learning, I don't have the knowledge to explain to them why it is a complicated thing to do.
So, in order to understand better the problem here, my questions are:
Is it is even possible to train a model using only 10 samples?
If possible, what is the appropriate cross-validation approach to use in such small dataset?
In order to explain to the group, why is it hard to get reliable results from small datasets?
Also, I'm trying to read as many articles as possible about ML, but the content is vast and I'm quite lost. Any recommendations of articles that discuss these problems that I pointed are very welcome.