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?

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

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


There's multiple type of baseline models:

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*A first try that you can compare yourself to, to see whether you are even doing anything meaningful, at all. E.g. a super-simple logistic regression linear model, or even just something as simple as predicting the average sale of the last month as the sale for the next day. If you do not beat or even do worse than such a baseline, there is a problem (either you have messed something up, or perhaps there is no useful information for making a prediction). This is mostly a sanity check to protect yourself from wasting a lot of effort without achieving anything even marginally useful.

*Very similar to 1, but using an option that many would regard as a basic default choice (e.g. LightGBM with parameters tuned using cross-validation for tabular data, modern convolutional neural network competently trained with suitable image augmentation for image classification etc.) without extra fancy add-ons (e.g. no model stacking of models from different model classes). An experienced person can put something like this together in as little as 1 day or perhaps up to a week (assuming it's a relatively standard use case, depending on the exact details and depending on how much the data/data loading still needs to be optimized etc.).

*A comparison baseline that you wish to beat (or at least be close to) to demonstrate that a new method is useful. This would typically be the currently best option (e.g. the system that is currently used in an industry example) / state of the art (for academic publishing purposes).

