4 ms·

But it comes down to this doesn't it: x1 * a + x2 * b + x3 * c + ... + x1000 * zzz + ... If a, b, c ... zzz, are all fixed constants already discovered by you

But it comes down to this doesn't it: x1 * a + x2 * b + x3 * c + ... + x1000 * zzz + ... If a, b, c ... zzz, are all fixed constants already discovered by your learning algorithm. That's a very fast calculation, and doesn't take anything like 50ms. Also, in the real world, you can establish a significance cutoff for a lot of these constants and get something like this as your final equation: x13 * m + x523 * cdf + x777 * wdc + x893 * ydz

jiofih 7y ago
The inputs to those functions might be coming from external data sources, or aggregated. These have a cost too. But mostly, it just adds up. At a thousand features, you have a 0.05ms budget for each. Without taking into account network latency since you won’t be running those models inside the application server.
- 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 ago
    Nobody'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!!!"

jiofih 7y ago

The inputs to those functions might be coming from external data sources, or aggregated. These have a cost too. But mostly, it just adds up. At a thousand features, you have a 0.05ms budget for each. Without taking into account network latency since you won’t be running those models inside the application server.

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 ago
  Nobody'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!!!"