HN user
There's lots written in the credit scoring space that I think other industries could look at - especially when it comes to calibration of models. It doesn't matter if the prediction is weak just as long as it is consistent over time periods. Banks rely on this consistency to ensure they are provisioning properly for losses.
It can be a AU$24 flat fee plus potentially any conversion fees for an intl transfer.
Could you provide some steps how you could bring everyone one board? We are still having python/R migration issues.
16 months old? How did the testing and overlay with PG's 18 startup mistakes go?
I'm curious about the real world implementation risk and if anyone has a methodology to proactively deal with external factors affecting the model performance. Such as if a feature is highly predictive of a certain outcome is there a framework to measure the volatility based on information outside of the dataset ie. product changes, marketing campaigns etc.