Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

ML is not that good at predicting consumers' choices

statmodeling.stat.columbia.edu

Post reply on HN
Print view View original

191–200 of 228 posts

Re: ML is not that good at predicting consumers' choices

#191

Post by wumpus »

sandworm101 wrote: post #180
vasco wrote: post #142

Earlier quoted context omitted.

Meanwhile if you fatten up by 20kgs you fly for the same price. Just eat your clothes!

The baggage weight limit has nothing to do with the weight of the aircraft. It is about keeping the bags light enough for handling, to protect the backs of baggage handlers (and automated equipment/belts). Unlike thier heavy bag, a heavy passenger is generally capable of walking themselves onto the plane.

Obviously you've never lived through having the weight balance of the baggage on your aircraft being deemed improper. 30 minutes to move to the other side of the airport, whereupon the weight balance is deemed OK, 30 minutes to return. Yes, Chicago.

Re: ML is not that good at predicting consumers' choices

#192

Post by soco »

wutbrodo wrote: post #152
Melatonic wrote: post #128

Earlier quoted context omitted.

The problem I think with all of these ML functions is that there is never enough of an in between full manual and full auto magic. The Nest could simply ask you a few times a day how you feel about how its doing (too hot ? too cold? too expensive?) or you could opt in somewhere and to give feedback. And then it could keep doing its behind the scenes mumbo jumbo.

This is more of a product problem than a technological one. I don't think the issue is lack of imagination, but hostility from consumers. There are a million and one cases where a pdocut could be 10x better with 10% more effort from me,and where that tradeoff is more than worth it (hell, it's why I use Linux). But the average consumer is aggressively turned off by having to do any "work", and the type of person hangi…

I have already a solution where I can put zero amount of work for optimizing it, it's called a button. Why would I bother with something as needy as this "smart" thing?

Re: ML is not that good at predicting consumers' choices

#193

Post by jstx1 »

wrycoder wrote: post #96
ape4 wrote: post #22

You just bought a washing machine... could I interest you in a washing machine?

I buy a package of underwear. All I see for next three weeks on my browser is close ups of men’s briefs. It’s embarrassing, when associates glance at my screen.

Kind of your fault - you're buying your underwear on a work device and you don't use an adblocker.

Re: ML is not that good at predicting consumers' choices

#194

Post by soco »

itissid wrote: post #164
Slackwise wrote: post #10

Earlier quoted context omitted.

"Why am I sweating right now? Oh, the Nest set the temperature too high again!" And then after a few instances, I just turn off all the automation and set up a schedule like normal. Same with the "away from home" which seems to randomly think I'm away and I have no idea why. Oh, and the app doesn't show me filter reminders, only the actual device, which I never touch all the way downstairs. There's not even any statu…

> Same with the "away from home" which seems to randomly think I'm away and I have no idea why. Away from home should be an easy problem to solve assuming Nest can talk to your phone(which is almost 100% true in real life). IN my experience there are several easy heuristics that can achieve ~ 90% precision and recall in home detection, like are you connected to a wifi or even combining it with some IMU data to be mor…

Everything should be an easy problem to solve yet it somehow stays a problem. For me it's quite binary: the problem is solved yes/no. And if the easy problem is still not solved, it's still not solved, period.

Re: ML is not that good at predicting consumers' choices

#195

Post by sandworm101 »

incone123 wrote: post #182
sandworm101 wrote: post #180

Earlier quoted context omitted.

The baggage weight limit has nothing to do with the weight of the aircraft. It is about keeping the bags light enough for handling, to protect the backs of baggage handlers (and automated equipment/belts). Unlike thier heavy bag, a heavy passenger is generally capable of walking themselves onto the plane.

If that's so, why is the luggage weight allowance applied cumulatively across multiple bags for one passenger? And why is it different between airlines and between ticket classes?

Because first class baggage is labelled and treated differently so it never gets bumped. The lower/higher numbers are also uses to force the penalty payments, something first class travellers don't want to deal with at check-in.

Re: ML is not that good at predicting consumers' choices

#197

Post by iudqnolq »

alpaca128 wrote: post #181
jltsiren wrote: post #45

Earlier quoted context omitted.

My favorite experience with Amazon: I had just preordered novel 9 of The Expanse, and I got an email recommending something else from the same authors: novel 8 of the Expanse. A more sensible recommendation engine might have assumed that someone who preorders part n+1 of a series may already have part n. Not to mention that Amazon should have known that I already had novel 8 on my Kindle. I guess generating personali…

> Not to mention that Amazon should have known that I already had novel 8 on my Kindle. Amazon doesn't seem to understand many things surrounding the Kindle. For example, it calculates the progress reading through a book by the last page I looked at. That means if I finished a book and jumped to the introduction it'll now be convinced I only read 1% of the book. This is so dumb, and I don't know why they even do it t…

It's surprisingly hard to make a better algorithm that properly supports people who re-read books.

Re: ML is not that good at predicting consumers' choices

#198

Post by macleginn »

wumpus wrote: post #185
stackbutterflow wrote: post #179

Earlier quoted context omitted.

You don't even that to go as high as 10,000. Imagine someone suddenly doing the job of 10 persons, that's entire teams being decimated. Go to a job board and imagine 9 of out 10 job postings not existing. How much harder it'll be to seek another job.

Decimated means 1/10 killed. I know people don't use it properly, but it's worth knowing.

Unfortunately, the previously incorrect usage won and it doesn't mean that anymore, according to major dictionaries: "kill, destroy, or remove _a large proportion of_" is given as the first definition in the Oxford dictionary used by Google. ("Unfortunately" is, of course, for people who have the original meaning activated because of how the word looks and/or know the historical context and feel the tension with the contextual meaning.)

Re: ML is not that good at predicting consumers' choices

#199

Post by DoingIsLearning »

wumpus wrote: post #185
stackbutterflow wrote: post #179

Earlier quoted context omitted.

You don't even that to go as high as 10,000. Imagine someone suddenly doing the job of 10 persons, that's entire teams being decimated. Go to a job board and imagine 9 of out 10 job postings not existing. How much harder it'll be to seek another job.

Decimated means 1/10 killed. I know people don't use it properly, but it's worth knowing.

To add completion to this, decimation was a Roman Legion punishment that was really high up in terms of severity.

The Centuria was a roman military unit of 100 men (The centuria size/meaning actually varied over time) but when the decimation punishment was applied that would mean that (by draw) every 10th men in the centuria would be killed, here is the catch, the people of their own centuria had to kill their own mates in decimation.

Re: ML is not that good at predicting consumers' choices

#200

Post by nojito »

mgraczyk wrote: post #58
nojito wrote: post #48

Earlier quoted context omitted.

>Always interesting to see outsiders writing papers about this I don't think you know who andrew gelman is. Additionally, that's not the conclusion derived from this study.

The actual conclusion of the study is so absurd that it's not worth engaging with seriously. That is, to maximally understand, and therefore predict, consumer preferences is likely to require information outside of data on choices and behavior, but also on what it is like to be human. I was responding to the interpretation from the blog post, which is more reasonable.

What’s "absurd" about it?
Post reply on HN