How simple should a Baseline model be? I was making baseline model and was wondering how much time I should spend on it.
I've found a lot of article about the purpose of a baseline model, for instance;

A baseline takes only 10% of the time to develop, but will get us 90% of the way to achieve reasonably good results.

But how do I properly select one?

*

*I'm I choosing among multiple algorithms/models?

*I'm I suppose to tune my simple model or leave it with its default?


You should tune the model if best practice application of that technique demands it.  The baseline needs to be competently implemented to be meaningful.
Hyper-parameter tuning can make a very significant difference, sometimes the difference between a state-of-the-art method and an un-competitive one, see e.g.
Anthony Bagnall, Gavin C Cawley "On the use of default parameter settings in the empirical evaluation of classification algorithms" arxiv
Just pick an algorithm you think would not be regarded as a straw man by an experienced practitioner (or reviewer 3 ;o) and apply it competently.  Do bear in mind that the better your baseline system, the more eye-catching the results if your proposed method is significantly better.
Caveat lector, Gavin Cawley is my alter-ego.
