Neural nets vs. regression models

Eliot Johnson writes:

I have a question concerning papers comparing two broad domains of modeling: neural nets and statistical models. Both terms are catch-alls, within each of which there are, quite obviously, multiple subdomains. For instance, NNs could include ML, DL, AI, and so on. While statistical models should include panel data, time series, hierarchical Bayesian models, and more.

I’m aware of two papers that explicitly compare these two broad domains:

(1) Sirignano, et al., Deep Learning for Mortgage Risk,

(2) Makridakis, et al., Statistical and Machine Learning forecasting methods: Concerns and ways forward

But there must be more than just these two examples. Are there others that you are aware of? Do you think a post on your blog would be useful? If so, I’m sure you can think of better ways to phrase or express my “two broad domains.”

My reply:

I don’t actually know.

Back in 1994 or so I remember talking with Radford Neal about the neural net models in his Ph.D. thesis and asking if he could try them out on analysis of data from sample surveys. The idea was that we have two sorts of models: multilevel logistic regression and Gaussian processes. Both models can use the same predictors (characteristics of survey respondents such as sex, ethnicity, age, and state), and both have the structure that similar respondents have similar predicted outcomes—but the two models have different mathematical structures. The regression model works with a linear predictor from all these factors, whereas the Gaussian process model uses an unnormalized probability density—a prior distribution—that encourages people with similar predictors to have similar outcomes.

My guess is that the two models would do about the same, following the general principle that the most important thing about a statistical procedure is not what you do with the data, but what data you use. In either case, though, some thought might need to go into the modeling. For example, you’ll want to include state-level predictors. As we’ve discussed before, when your data are sparse, multilevel regression works much better if you have good group-level predictors, and some of the examples where it appears that MRP performs poorly, are examples where people are not using available group-level information.

Anyway, to continue with the question above, asking about neural nets and statistical models: Actually, neural nets are a special case of statistical models, typically Bayesian hierarchical logistic regression with latent parameters. But neural nets are typically estimated in a different way: the resulting posterior distributions will generally be multimodal, so rather than try the hopeless task of traversing the whole posterior distribution, we’ll use various approximate methods, which then are evaluated using predictive accuracy.

By the way, Radford’s answer to my question back in 1994 was that he was too busy to try fitting his models to my data. And I guess I was too busy too, because I didn’t try it either! More recently, I asked a computer scientist and he said he thought the datasets I was working with were too small for his methods to be very useful. More generally, though, I like the idea of RPP, also the idea of using stacking to combine Bayesian inferences from different fitted models.