I like this post. I haven't had time to dig into Jev (they aren't accepting new signups), but calibrated probabilities is one of their pitches that caught my attention. And I was wondering how does one offer them on user data. Standard calibration essentially ensures that if a score of 0.8 accompanies a positive prediction (assuming the simple case of binary classification), then if you gathered together all predictions with a score of 0.8, around 80% will be correct.
If you have just one example you're sending to a model, how would they guarantee 80% over your data?
FYI, for an overview, scikit's page on calibration is great [1], and my answer on Quora from a long time ago covers a specific type [2].
abhgh · · focus · HN ↗
If you have just one example you're sending to a model, how would they guarantee 80% over your data?
FYI, for an overview, scikit's page on calibration is great [1], and my answer on Quora from a long time ago covers a specific type [2].
[1] <a href="https://scikit-learn.org/stable/modules/calibration.html" rel="nofollow">https://scikit-learn.org/stable/modules/calibration.html
[2] <a href="https://www.quora.com/How-is-isotonic-regression-used-in-practice-for-calibration-in-machine-learning/answer/Abhishek-Ghose" rel="nofollow">https://www.quora.com/How-is-isotonic-regression-used-in-pra...
edot · · focus · HN ↗
abhgh · · focus · HN ↗