[eVoLInTHRo]

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.

[colinmhayes]

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.

[mr_toad]

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).

[chandra381]

this is a great explanation. Thank you