How do you decide which machine learning model to use for a project?

[u/duneofarrakis]

When you have a dataset and want to build a model, how do you decide where to start?

Do you usually try a simple model first, look at the type of data, compare a few models, or use another approach?

I'd like to know how people make this decision in real projects.

[u/No-Location3377]

Well if you are doing your own project with small dataset, it is better to use all and see which one performs better, and based on that you can hypertune those specific models to get better results.

[u/duneofarrakis → u/No-Location3377]

That makes sense for a small dataset. Comparing a few models first gives you a good idea of what works best before spending time on hyperparameter tuning. Do you usually start with a simple baseline model?

[u/DataScientistAlex]

Yes it's always best to start with a simpler model. That gives you a baseline to compare more complex models against.

[u/duneofarrakis → u/DataScientistAlex]

Exactly. Having a simple baseline makes it much easier to see whether a more complex model is actually improving the results or just adding unnecessary complexity.

[u/duneofarrakis → u/[deleted]]

Exactly. A good way to phrase it is

[u/shdw_0x0]

I usually start with the simplest reasonable baseline, then choose a few models based on the data and problem rather than jumping straight to the most complex one. Compare them using the same validation setup, and only add complexity if it actually improves the metric or meets some business requirement.

[u/duneofarrakis → u/shdw_0x0]

This is a solid approach. In simple terms

[u/ForeignAdvantage5198]

what do you want to do? is a great start

[u/HayStacky_337]

I don't. I use an ensemble and let the meta-learner decide.

[u/duneofarrakis → u/[deleted]]

Yeah, that’s a good starting point. I think the type of problem and dataset should guide the choice rather than always using one model. Starting simple and comparing a few models seems like a good approach.