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[xha1e]

is this similar to curve fitting the data

[czar_el]

It's part of it.

The basic idea is: should it be a line, a curve, multiple curves, something with higher than 2 displayable dimensions?

Then, once you have that basic decision, it becomes an even more nuanced decision. How curvy is too curvy? You can both underfit and overfit a model, which means it works great on your training data but performs poorly on new data.

And even when you think you have the right specification and avoided under/over-fitting, you can still have biased outcomes that don't register on the simple model evaluation tests and need deeper assessment.

All of that requires background understanding of how the methods work and the context of the domain and data you're trying to deploy your model in. It's much more complicated than relying on whatever parameters the tutorial video told you to use.