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I wonder if this thing is used for Uber Eats in Singapore... Because I use User Eats all the time, the estimated time is MASSIVELY inaccurate. It's not even wit
by philliphaydon 9y ago
I wonder if this thing is used for Uber Eats in Singapore... Because I use User Eats all the time, the estimated time is MASSIVELY inaccurate. It's not even within 30% to be close to actual time.
The estimated time is always less than the actual time, but every minutes the estimated time re-adjusts. By the time it's actually delivered it's about 15 to 20 minutes more than the original estimate.
Note: I'm just complaining because the article uses User Eats as an example and as a service I use often I wonder if this ML Platform is used at all...
- strin 9y agoMaybe they trained on global data and it biases against sample portion of the dataset that represents Singapore?
- philliphaydon 9y agoWouldn't the ML learn to that times differ globally tho?
- d4rti 9y agoOnly with the right models and features.
- Quarrelsome 9y agoML is only as good as its inputs and if it doesn't have any Singaporean inputs then its gonna potentially suck for Singapore if its an outlier.
- jdwyah 9y agoChuckle. Same experience. As far as I can tell the estimated time is about as good as the Windows 3.1 file copy time estimation.
- anindha 9y agoMaybe they did use ML in your scenario just not the way you thought: It takes 50 mins to get your delivery but to make you order they need to tell you its going to take 30 mins. ML also tells them that the 20 minute discrepancy won't stop you ordering next time. Cycle continues.