Why use learning when you can fit?

http://databasearchitects.blogspot.com/2019/05/why-use-learning-when-you-can-fit.html

by ngaut • 7 years ago

22 8 7 years ago

databasearchitects.blogspot.com

enriquto3• 7 years ago

This question makes no sense to me. Isn't learning a form of fitting?

aisofteng• 7 years ago

In machine learning, a learned model should be able to generalize to unseen data. Curve fitting does not have that characteristic.

kranner• 7 years ago

Curve fitting does generalise to unseen data, and can even do it well if the training data was representative of the test data.

enriquto3• 7 years ago

I do not understand what you mean. The whole point of curve fitting is to evaluate your curve at points that were not seen.

8note• 7 years ago

as long as you're interpolating, it should do just fine.

if you're extrapolating, it'll depend on whether you've assumed the right boundary conditions, but that's true for any model

darsnack• 7 years ago

You’re right, but that is the motivation for the blog post. Learning is a form of fitting that performs better in practice than the curve fitting mentioned in the blog post when the required fit is nonlinear and/or high dimensional. The author is making the point that if the model is simple, since learning is just a form of fitting, why not directly apply standard curve fitting techniques?

joycian• 7 years ago

I don't get this article? Fitting the polynomial is learning, right? (fancy machine learning methods are also not much more than curve fitting)

Why use learning when you can fit?

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