What Data Can’t Do • Hacker News Board

Re: What Data Can’t Do

#41

Post by bluGill » Wed, Mar 31, 2021, 4:41 PM UTC

kazinator wrote: post #23

> doctors would be given a financial incentive to see patients within forty-eight hours. Not measuring that from the first contact that the patient made is simply dishonest. "Call back in three days to make the appointment, so I can claim you were seen within 48 hours, and therefore collect a bonus" amounts to fraud because the transaction for obtaining that appointment has already been initiated. I mean, they could…

On hindsight. However did you think of that before hearing of the problem. Even if you did, can you think of - without much time to think - how every possible metric I can propose on every possible topic.

Tony Blair was trying to solve a real problem that needed solving. That he opened a different problem is something that we should think of as normal, and not blame him for trying to solve the original problem. The question should be how to we change the metric until the unintended consequences are ones we can live with. That will probably take more than a lifetime to work out.

Note that there will be a lot of debate. There are predicted consequences that don't happen in the real world for whatever reason. There are consequences that some feel we can live with that others will not accept. Politics is messy.

Re: What Data Can’t Do

#42

Post by [deleted] » Wed, Mar 31, 2021, 4:41 PM UTC

Eridrus wrote: post #39

kazinator wrote: post #23

> doctors would be given a financial incentive to see patients within forty-eight hours. Not measuring that from the first contact that the patient made is simply dishonest. "Call back in three days to make the appointment, so I can claim you were seen within 48 hours, and therefore collect a bonus" amounts to fraud because the transaction for obtaining that appointment has already been initiated. I mean, they could…

It's really hard to design a not game-able metric. The problem here seems to be that doctors are under-provisioned for some reason, and so long wait times are a form of load shedding for the system. Without addressing this core issue, which individual clinics have little control over because they are generally boxed in by regulations over who can administer medical care, except to rush appointments (which they're pro…

[deleted]

Re: What Data Can’t Do

#43

Post by carlosf » Wed, Mar 31, 2021, 4:47 PM UTC

currymj wrote: post #28

carlosf wrote: post #4

I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…

i think this actually gets at what makes applied ML distinct from statistics as a practice, even though there is a ton of overlap. statisticians make assumptions 1 and 2, and think of themselves as trying to find the "correct" parameters of their model. people doing applied ML typically assume they don't know 1 (although they might implicitly make some weak assumptions like sub-gaussian to avoid fat tails, etc.) and…

I disagree, every ML model has some implicit statistical assumption, which is often not well understood by practitioners.

At minimum you must assume your underlying process is not fat tailed. If it is, then your training/validation/test data might never be enough to make reliable predictions and your model might break constantly in prod.

BTW shifting distributions and fat tailed distributions are sort of equivalent, at least mathematically.

Re: What Data Can’t Do

#44

Post by 6gvONxR4sf7o » Wed, Mar 31, 2021, 4:47 PM UTC

currymj wrote: post #28

carlosf wrote: post #4

I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…

i think this actually gets at what makes applied ML distinct from statistics as a practice, even though there is a ton of overlap. statisticians make assumptions 1 and 2, and think of themselves as trying to find the "correct" parameters of their model. people doing applied ML typically assume they don't know 1 (although they might implicitly make some weak assumptions like sub-gaussian to avoid fat tails, etc.) and…

> instead you assume you have some reliable way of evaluating performance on the task you care about -- usually measuring performance on an unseen test set. as long as this is actually reliable, then things are fine.

This is the part that often fails in practice. Think of all the benchmarks that show superhuman performance and compare that to how good those same models really aren't. Constructing a good set of holdouts to evaluate on is really hard and gets back to similar issues. In practice, doing what you're describing reliably (in a way that actually implies you should have confidence in your model once you roll it out) is rarely as simple as holding out some random bit of your dataset out and checking performance on it.

On the other hand, what you often see is people just holding out a random bunch of rows.

Re: What Data Can’t Do

#45

Post by rademacher » Wed, Mar 31, 2021, 4:56 PM UTC

carlosf wrote: post #4

I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…

The problem is high dimensions knowing the distribution or even characterizing it fully with data is incredibly difficult (curse of dimensionality). I think the real assumption in ML is just that there is some low dimensional space that characterizes the data well and ML algorithms find these directions where the data is constant.

Re: What Data Can’t Do

#46

Post by tomrod » Wed, Mar 31, 2021, 4:57 PM UTC

An absolutely fantastic article that captures my concerns as a user, purveyor, and automater of systems that help with numbers. I'm always very cautious regarding the jump from numbers informing to numbers deciding.

Re: What Data Can’t Do

#47

Post by quietbritishjim » Wed, Mar 31, 2021, 5:12 PM UTC

ppod wrote: post #34

This author has published a couple of articles like this at the New Yorker They all have this in common: the author works through some interesting and in some ways unusual cases where data or statistics have been improperly or naively applied, with some social costs. I really enjoy the articles themselves. Then the New Yorker packages it up with a cartoon and a headline and subheadline like "Big Data: When will it ea…

Hannah Fry is mathematics communicator working in quite a few other media. She's been on a couple of BBC documentaries, and on a few videos on the Numberphile YouTube channel (which is also very good regardless of who's on it)

Re: What Data Can’t Do

#48

Post by ectopod » Wed, Mar 31, 2021, 5:12 PM UTC

kazinator wrote: post #23

> doctors would be given a financial incentive to see patients within forty-eight hours. Not measuring that from the first contact that the patient made is simply dishonest. "Call back in three days to make the appointment, so I can claim you were seen within 48 hours, and therefore collect a bonus" amounts to fraud because the transaction for obtaining that appointment has already been initiated. I mean, they could…

Not to defend the system, but I don't think they were trying to fraudulently get her 48 hour payment. Rather, if they accepted advance bookings (which was, after all, the old system) then almost nobody would be seen in 48 hours. They could have offered advance bookings for follow-ups, but since most appointments are taken by the sickest people, many of them will be follow-ups, so this probably wouldn't have helped.

If you want to reduce the queuing time in a system you need to reduce the processing time (i.e. the duration of an appointment) or increase the number of servers (i.e. doctors). You can't do it by edict.

Re: What Data Can’t Do

#49

Post by polytely » Wed, Mar 31, 2021, 5:13 PM UTC

strathmeyer wrote: post #36

polytely wrote: post #15

Earlier quoted context omitted.

Did you use reader mode? because I noticed that when I use firefox's reader mode it will cut off part of the article on the New Yorker site.

We clicked on the link and were presented with two paragraphs. New Yorker articles usually don't show up correctly, I don't know why they allowed to be posted here. Acting like we don't know how to read a webpage is gaslighting.

Well that wasn't my intention at all, just a problem i encountered a few hours before I read your comment so it was fresh on my mind.

Re: What Data Can’t Do

#50

Post by sfink » Wed, Mar 31, 2021, 5:18 PM UTC

carlosf wrote: post #4

I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…

Personally, I think the main problem with ML is simpler: it works well for interpolation, and is crap for extrapolation.

If the outputs you want are well within the bounds of your training data set, ML can do wonders. If they aren't, it'll tell you that in 20 years everyone will be having -0.2 children and all the other species on the planet will start having to birth human babies just so they can be thrown into the smoking pit of bad statistical analysis.