Econometrics is the application of statistical techniques on economics-related problems, typically to understand relationships between economic phenomena (e.g. income) and things that might be associated with it (e.g. education).

Machine learning is typically defined as a way to enable computers to learn from data to accomplish tasks, without explicitly telling them how.

Both fields can use logistic regression, regularization, and gradient descent to accomplish their goals, so in that sense there's no distinction.

But IMO there is a difference in their primary intention: econometrics typically focuses on inference about relationships, machine learning typically focuses on predictive accuracy. That's not to say that econometrics doesn't consider predictive accuracy, or that machine learning doesn't consider inference, but it's usually not their primary concern.

So you're going with the only difference being who's building the model. Interesting take, can't say I disagree much. Although I would say that regularization in econometric models is a bit rare because it distorts the coefficients which as you pointed out is the primary goal of econometrics.

Econometric models tend to be hand-fit and focus more on explanation/hypothesis testing than prediction, so automated variable selection is less common (and sometimes frowned upon).

this is a great explanation. Thank you

From my experience, econometricians and ML practitioners mostly pretend like the other group doesn't exist.

Well, what do you define as machine learning?

Logistic regression is clearly a classifier. And you need data to train it. So it's a supervised learning algorithm.

I'm trying to have a conversation so I can figure it out. Pretty confident that being a classifier does not make it machine learning, econometrics has classifiers too. Econometric models also need data to train them, so I'm not sure your second point is helpful either. Unless you're claiming the difference is nothing but whether the model is used by an economist.

What is machine learning then?

Statistics as practiced by computer scientists :)

More generally, I recommend Breiman's two cultures article for some insight into the similarities and differences.

If you need a really simple explanation, then machine learning is a tool for generating predictions, while statistics is a method for performing inference about causes.

I think you'll enjoy this delightful thread: https://stackoverflow.com/questions/4205105/whats-the-differ...

The correct bucket for logistic regression should be "statistics", under "generalized linear models".

Logistic regression can use both L1 and L2 regularization

And ols can too. That doesn't make it machine learning. This implementation doesn't involve any regularization.

I am not really sure why you are trying to redefine the term machine learning with random references to regularization. OLS is taught in every machine learning course. The machine learns parameters via gradient descent for a wide variety of loss functions.