4 ms·
So why not load the calculated constants to the application server to reduce network latency? And the learning side of things should have culled that list of t
by romaaeterna 7y ago
So why not load the calculated constants to the application server to reduce network latency?
And the learning side of things should have culled that list of thousand features down to a list of 5 - 10 that mattered.
It really sounds like the off-the-shelf stuff isn't built for efficiency.
- deleted 7y ago[deleted]
- Breza 7y agoNobody's saying this is an impossible problem. The paper shows how much additional work is required beyond a traditional data science workflow. The team behind the paper built a model that had good performance on training data. They're a smart lot so they knew they needed to cross-validate. The results held up in cross-validation! Hooray, the model works! ...right? That's as far as a lot of data scientists go. This paper points out that you need to have a model that does (at least) three things: 1. Generates good scores with training and testing data 2. Outperforms existing models in the real world 3. Runs really really quickly There are a lot of data scientists who have no idea how to do #2 and #3. This paper says "These parts are really important!!!"