31–40 of 197 posts
The whole point is to match abandoned animals with suitable homes. Adoption speed seems secondary to post adoption measures of adopter satisfaction and adoptee welfare.
Never understood Kaggle. The Netflix Prize was great. Now it’s just people gaming Kaggle to get a job.
The Netflix Prize winners regularly compete on Kaggle. One of the top 10 Netflix Prize winners joined Kaggle early on (he got a job out of it). Kaggle competitions are a game. Most notorious players already have a good job.
> These predictions would be used to optimize and tweak future critters' profiles so that they are adopted as soon as possible.
Sorry but, how is this useful? You can't just change the age of an animal to make it more likely to be adopted. The profile is meant to be an accurate representation of the animal so people know what they're getting. What exactly was the algorithm meant to achieve aside from being a predictor?
I remember reading about this on Twitter because of a reply from h2o.ai account. https://twitter.com/ppleskov/status/1215983188876709888?s=19 The person was originally employed at h2o.ai and as a consequence of this was fired. Not sure if that was completely appropriate. Wasn't this a personal participation? Or are there "company teams" on Kaggle ?
> The goal was to create an algorithm that could predict how quickly a pet would be adopted based on its profile details, from its photo to its breed, sex, size, age, and whether it had been vaccinated or not. > These predictions would be used to optimize and tweak future critters' profiles so that they are adopted as soon as possible. Sorry but, how is this useful? You can't just change the age of an animal to make…
Competive ML model grading with a common training set using unseen data.
Cheat was to scrape the data that would then be used as unseen by the organisers. Unseen is now seen for this model. Then, instead of training the model with the "unseen" data, which would have been cheating and an advantage it apparently wasn't enough of an advantage so they hard code 10% of the cases to boost metrics and win.
Having more data to train your model is google & facebricks competitive advantage. Their attempts to use that advantage for something actually useful to society rather than just as a method of selling ads seems to have been a complete bust so far. If that is wrong you know better, please link us up.
I'm suspicious that their predictive power to sell ads actually works for the people who buy those ads but I guess we aren't likely to know for sure. I do wonder "who dominated their industry segment in sales by being an early adopter of google ads" I don't know anyone. It's not a great metric but what else do we have?
Earlier quoted context omitted.
The Netflix Prize winners regularly compete on Kaggle. One of the top 10 Netflix Prize winners joined Kaggle early on (he got a job out of it). Kaggle competitions are a game. Most notorious players already have a good job.
I think the original intent was great. And you can learn from it still. However, as the job market for data scientists heated up, Kaggle became something else entirely.
> The goal was to create an algorithm that could predict how quickly a pet would be adopted based on its profile details, from its photo to its breed, sex, size, age, and whether it had been vaccinated or not. > These predictions would be used to optimize and tweak future critters' profiles so that they are adopted as soon as possible. Sorry but, how is this useful? You can't just change the age of an animal to make…
Exactly my thought when I read this also, and I think it generalizes to a lot of cases where the value of trying to predict something is dubious at best, when there's nothing obvious you can do with the prediction itself.
I think one of the biggest skills to have in the ML space, is knowing what is worth training a model to know, and what isn't. Just like in engineering, the most successful products are those that solve a real world problem, no matter how elegantly the others might have been made.
I never understood Kaggle. Most competitions don't require code to be submitted, just predictions to be made on a test set with missing labels. So, you don't even need to apply machine learning and I'd bet money that lots of winners don't and label by hand or outsource. I don't understand the fascination and appeal of these so-called "grandmasters".
Your point of view is outdated ;-)
In recent ML competitions, participants do submit code that is run on a held-out dataset - as was the case in the PetFinder.my challenge in question here.
Most competition platforms are migrating to this format, as otherwise you can just label by hand as you said.
Note that this competition went even further: not only was the evaluation code run on Kaggle, the training code was also run there. This means that you couldn't even train a gigantic model then submit it: your model had to be trainable within well defined time and resource constraints, which is a great way to level the playing field.
Of course, there's still some unfairness as people with more resources can try out more solutions before submitting a model to be trained on the platform. No platform has a solution for this yet!
> The goal was to create an algorithm that could predict how quickly a pet would be adopted based on its profile details, from its photo to its breed, sex, size, age, and whether it had been vaccinated or not. > These predictions would be used to optimize and tweak future critters' profiles so that they are adopted as soon as possible. Sorry but, how is this useful? You can't just change the age of an animal to make…