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?


I agree (+1) with the answers by Stephan Kolassa and Dikran Marsupial, but let me add my two cents.
What you need to consider as well is what is your target model. For example, for a classification task logistic regression may be a good benchmark, but if your actual model is logistic regression, you obviously would choose something simpler than that. Usual choice would be a simple model that is known to work for such problems, for example naive forecast for time-series, mentioned by Stephan Kolassa, or LSTM model for NLP task. If you were building a novel state-of-the-art model, you would benchmark against other state-of-the-art models. On another hand, it is always useful to have a trivial benchmark as a sanity check (predict mean, median, the most common value, last value, etc), because there is always risk that your simple benchmark is not good itself.
