[P] My blog on ML model evaluation (Bayes optimal decisions, ROC curve, LLR calibration)

I have published 3 articles about ML model evaluation on my personal blog. Just finished the 3 installment, so I am keen to share and get some feedback.

I cover frameworks traditionally used in ML like ROC curves, but from a Bayes decision perspective, which I have been struggling to find in textbooks/tutorials. The 3rd part is about the evaluation of log-likelihood calibrated models.

Hope you will find it interesting/useful!

[https://mkffl.github.io/2021/10/18/Decisions-Part-1.html](https://mkffl.github.io/2021/10/18/Decisions-Part-1.html)
[https://mkffl.github.io/2021/10/28/Decisions-Part-2.html](https://mkffl.github.io/2021/10/28/Decisions-Part-2.html)
[https://mkffl.github.io/2022/03/02/Decisions-Part-3.html](https://mkffl.github.io/2022/03/02/Decisions-Part-3.html)
And the underlying code for reproducibility [https://github.com/mkffl/decisions](https://github.com/mkffl/decisions)

bayes blog decisions evaluation machinelearning ml roc