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Comment by tomelders | Hacker News Reader

Im currently building a UI for an app that asks the user to input a ridiculous amount of info in exchange for an accurate trade in price on a vehicle. I mentioned to the client that this is a classic linear regression problem and that they could pribably get more accurate estimates with far fewer input variables. Also, their backend datastore is in a real mess and is going to be a constant source of problems going forward.

But they simply weren't interested in even considering it. They just do not believe that machine learning can out perform their current process.

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On a tangential note, "The robust beauty of improper linear models in decision making"[1] is probably one of my favorite papers on modeling and decision research.

> Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method; for example, they may be obtained on the basis of intuition, derived from simulating a clinical judge's predictions, or set to be equal. This article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors.

[1] http://www.niaoren.info/pdf/Beauty/9.pdf

I studied this a couple of years back. You can even prove error bounds.

For example, using Jensen's inequality, I was able to prove that a unit weighted linear model will get the same result as the optimal linear model 75% of the time on average, provided the features are independent normally distributed gaussians.

https://www.chrisstucchio.com/blog/2014/equal_weights.html

I haven't figured out how to prove the same result for binary features, but simulations suggest that will work also.

> prove that a unit weighted linear model will get the same result as the optimal linear model 75% of the time on average

This is such an important result & yet doesn't seem to be a widely known result. Your proof is very well done. You should consider publishing it in CMJ. Or if you don't mind, I'd be happy to rework it & send it in :) Thanks for writing it up.

I'm not familiar with CMJ but happy to discuss the idea. Write to me, email is in my profile.

As a paper it would be significantly stronger if it did the binary case, though.

Hey thanks! I prefer the CMJ (http://www.maa.org/press/periodicals/college-mathematics-jou...) to AMM etc. because your proof is ultimately undergrad math - some calculus, complex analysis, change of coords and inequalities. It's a pity the CMJ is not as well known. My professor used to circulate copies in class and we learnt more from it than from the grad journals which went out of the way to make things abstract. You also have a fantastic blog on Harris Benedict equation, which I have combined with an ML approach on WeightGreat - truth be told, this was one of those miracles that happen when you read Hacker News regularly. You find someone has worked on the exact same problem you are interested in, using a different approach, and you can combine that with your own insight to come up with a very promising idea.

I'll take a look. And send me an email, what you are doing sounds interesting, would love to learn more.

That is surprising. I wouldn't've guessed 75%. I would've guessed it could be arbitrarily close to 50%.

I haven't read the paper but based on your extract are such improper models more susceptible to black swan events? I'd suspect a more clinical model might actually be even more susceptible (more variables means a greater chance of some variables being wrong and/or missing correlations between variables which would amplify a wing variable to a greater degree) but I'm curious if this aspect had been explored.

Most people get scared by things they cant visualize. I have a friend at a large, popular tech company. He was tasked with solving a problem that a linear regression was exactly perfect for. The thing they were trying to model was even known to be linear! His boss wouldn't let him do it because he couldn't visualize a best plane.

Instead, he had to do a series of best fit lines in the various possible 2D projections. At the end, he showed the boss that a linear regression provided the same result and the boss was still untrusting.

"Nobody ever gets fired for contracting IBM."

Your friend's boss doesn't sound like he should be managing data scientists or ML engineers

Heh. I have exactly the opposite problem. One of my senior product managers is crazy in love with the concept of ML, despite knowing absolutely nothing about it beyond what he's read in some puff pieces in business magazines. Any time I run into any non-trivial problem his first suggestion is often "What's so hard about that? Just collect a bunch historical data and feed it into a machine learning algorithm (in the cloud!)".

edit: Also his definition of "Big Data" seems to be "more data than can be printed on an A4 piece of paper"

Where do you find these people? I have plenty of ML to sell them.

I'm joking of course, but I'm also quite curious: in which sector(s) does this seem to happen the most? My clients have been weary of ML hype, actually.

> But they simply weren't interested in even considering it. They just do not believe that machine learning can out perform their current process.

Right here is where a startup could be born...

I considered it, but the used car business is something I have absolutely no desire to get into. Fill your boots.

Are you sure they gave you the right reason? Or do they want the user to input the data, so they can use it for some other analysis?

> Ridiculous amount of info

Just to understand, when you said "ridiculous" you mean "a few" or "a lot" of?

> that they could pribably get more accurate estimates with far fewer input variables

suggests it meant "a lot"

He definitely means a lot.

About 50 fields. And a lot of those fields are really difficult for your average person to know how to answer.

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