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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 mention

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.

tnecniv 10y ago
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.
- busterarm 10y ago
  "Nobody ever gets fired for contracting IBM."
- nerfhammer 10y ago
  Your friend's boss doesn't sound like he should be managing data scientists or ML engineers
nhf 10y ago
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 http://www.niaoren.info/pdf/Beauty/9.pdf
- yummyfajitas 10y ago
  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 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.
  - deleted 10y ago
    [deleted]
  - dxbydt 10y ago
    > 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.
    - yummyfajitas 10y ago
      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.
      - dxbydt 10y ago
        Hey thanks! I prefer the CMJ (http://www.maa.org/press/periodicals/college-mathematics-journal/the-college-mathematics-journal 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.
omginternets 10y ago
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.
edpichler 10y ago
> Ridiculous amount of info Just to understand, when you said "ridiculous" you mean "a few" or "a lot" of?
- detaro 10y ago
  > that they could pribably get more accurate estimates with far fewer input variables suggests it meant "a lot"
- simonswords82 10y ago
  He definitely means a lot.
- tomelders 10y ago
  About 50 fields. And a lot of those fields are really difficult for your average person to know how to answer.
harryf 10y ago
> 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...
- tomelders 10y ago
  I considered it, but the used car business is something I have absolutely no desire to get into. Fill your boots.
annnnd 10y ago
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?
dagw 10y ago
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"

tnecniv 10y ago

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.

busterarm 10y ago
"Nobody ever gets fired for contracting IBM."
nerfhammer 10y ago
Your friend's boss doesn't sound like he should be managing data scientists or ML engineers

nhf 10y ago

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 http://www.niaoren.info/pdf/Beauty/9.pdf

yummyfajitas 10y ago
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 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.
- deleted 10y ago
  [deleted]
- dxbydt 10y ago
  > 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.
  - yummyfajitas 10y ago
    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.
    - dxbydt 10y ago
      Hey thanks! I prefer the CMJ (http://www.maa.org/press/periodicals/college-mathematics-journal/the-college-mathematics-journal 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.

omginternets 10y ago

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.

edpichler 10y ago

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

detaro 10y ago
> that they could pribably get more accurate estimates with far fewer input variables suggests it meant "a lot"
simonswords82 10y ago
He definitely means a lot.
tomelders 10y ago
About 50 fields. And a lot of those fields are really difficult for your average person to know how to answer.

harryf 10y ago

> 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...

tomelders 10y ago
I considered it, but the used car business is something I have absolutely no desire to get into. Fill your boots.

annnnd 10y ago

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?

dagw 10y ago

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"