Z1ndabad t1_j5hbncl wrote on January 23, 2023 at 12:24 AM

Hey guys, new to ML and cant seem to wrap my head around the concept. I was to make a used car price prediction model using large data set and most of the tutorials i watch just use the linear regression library. However can you use neural networks instead like Levenberg-marquat?

trnka t1_j5k5ndr wrote on January 23, 2023 at 4:16 PM

Yeah you can use a neural network instead of linear regression if you'd like. I usually start with linear regression though, especially regularized, because it usually generalizes well and I don't need to worry about overfitting so much.

Once you're confident that you have a working linear regression model then it can be good to develop the neural network and use the linear regression model as something to compare to. I'd also suggest a "dumb" model like predicting the average car price as another point of comparison, just to be sure the model is actually learning something.

I'm not familiar with the Levenberg–Marquardt algorithm so I can't comment on that. From the Wikipedia page it sounds like a second-order method, and those can be used if the data set is small but they're uncommon for larger data. Typically with a neural network we'd use an optimizer like plain stochastic gradient descent or a variation like Adam.

Oceanboi t1_j5n6ed0 wrote on January 24, 2023 at 4:32 AM

Can you expand on why one might ever want to apply a neural network to linear regression? It feels like bringing a machine gun to a knife fight.

trnka t1_j5nukd2 wrote on January 24, 2023 at 9:15 AM

I'm not sure what you mean by applying a NN to linear regression.

I'll try wording it differently. Sometimes a NN can outperform linear regression on regression problems, like in the example if there's a nonlinear relationship between some features and car price. But neural networks are also prone to over-fitting so I recommend against having a NN as one's first attempt to model some data. I recommend starting simple and trying complex models when it gets difficult to improve results in simple models.

I didn't say this before but another benefit of starting simple is that linear regression is usually much faster than neural networks, so you can iterate faster and try out more ideas quickly.