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Comment by jc4p | Hacker News Reader

I think a lot of the real benefits from ML "at work" is more in just cleaning of data and running through the gauntlet of simplest regressions (before jumping onto something more magical whose outputs and decision making process you can't exactly explain to someone).

I would classify something like this blog post as ML, would you? http://stackoverflow.blog/2016/11/How-Do-Developers-in-New-Y...

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When people talk about the growth (or sometimes 'excess') of ML solutions these days, I always wonder about this.

A basic linear regression probably isn't ML, a backprop neural net clearly is, but somewhere between the two is a very fuzzy line between "statistics and data cleaning" and "actually machine learning". I think a lot of people have just pushed the ML angle of an already-reasonable approach to tie into that popularity.

I think these distinctions are akin to discussing musical genre in Youtube comments. Something can be all of: mathematics, advanced IT, computer science, data science, statistics, econometrics, control theory, pattern recognition, machine learning, predictive modeling, data mining, data analysis, data engineering, or even AI if you are feeling fancy.

ML courses often start with linear regression, and if you build up complicated polynomials to find a nonintuitive model of your problem, I would definitely consider that machine learning.

I wouldn't. I'd call it "basic computational statistics." But I think I might be in the minority on that.

I've always thought of regressions, even high-order ones, as just a statistical tool. They're present at the start of ML courses, sure, but as a tool used in ML techniques or a good alternative to them.

It looks like that's not the standard view, though.

The whole deal seems weird to me.

Neural networks are just functional approximators, so why isn't a linear regressor of k-th order (e.g. Taylor expansion up to k-th order) also considered "ML"? What's the distinction here?

I like this definition of ML.

Off topic: somehow the people not doing data cleaning because it is so boring and ML techniques do not need clean data end up with the worst overfitting I have ever seen.

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