[lacksconfidence]

Being that this is a SVM, which is typically evaluated as a simple linear sum of weights, I imagine they reimplemented that in the application layer. Would be curious how they handled the normalization steps (reimplement that as well?)

[jliszka]

Yep. We normalize our features as part of training, and the stdevs of each feature are part of the resulting model, along with the weights. (The means are always 0 because of the way we construct our training set.) The weights we use in production are actually normalized_weight / stdev.