3 ms·

To add to this. One of the best thing about this approach is how little computational power it requires compared to NNs. I was able to finish this project in li

To add to this. One of the best thing about this approach is how little computational power it requires compared to NNs. I was able to finish this project in little time without having to get extra hardware approved. One core of our DB is now constantly used to feed real time data to constantly retrain the models but that, plus a partial core doing the analysis is all it takes to analyze billions in transactions per year in near real time and keep our predictions up to date and live on our portal. I'm also not too worried that I could get better performance using NNs as Bayesian methods are basically proven to be near optimal under some fairly reasonable assumptions and the more automatic methods overfitting or latching on artifacts of imperfect inputs actually have worse performance as the article points out. Until you get to really high dimensional problems, this is a better approach imo.

hackerlight 4y ago
With these partially hand built trees, are you using only 1 or 2 at inference time? Or is it still a full on large forest of 200 trees? To elaborate: Are you coding up this bayesian logic as part of the training process, and then you run this code 200 times to end up with 200 trees? That code mixes the hand built rules with the normal approach to tree construction (random split location, random feature choice out of N features, etc)
- BenoitEssiambre 4y ago
  In this case I ended up with two very simple trees which I train for each ATM (with a prior trained on the larger fleet). You could try a mix though. I didn't have time to try too many things. The following step which I didn't mention, was to use the trees for generating predictions into the future. Little monte carlo simulations using one of the tree (selected using that relative entropy methods, you could even use a weighted mix of both trees) that I run a few thousand times for each ATM and to generate a forecasted range of when the ATM is going to run out of cash.

hackerlight 4y ago

With these partially hand built trees, are you using only 1 or 2 at inference time? Or is it still a full on large forest of 200 trees? To elaborate: Are you coding up this bayesian logic as part of the training process, and then you run this code 200 times to end up with 200 trees? That code mixes the hand built rules with the normal approach to tree construction (random split location, random feature choice out of N features, etc)

BenoitEssiambre 4y ago
In this case I ended up with two very simple trees which I train for each ATM (with a prior trained on the larger fleet). You could try a mix though. I didn't have time to try too many things. The following step which I didn't mention, was to use the trees for generating predictions into the future. Little monte carlo simulations using one of the tree (selected using that relative entropy methods, you could even use a weighted mix of both trees) that I run a few thousand times for each ATM and to generate a forecasted range of when the ATM is going to run out of cash.