Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.
Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.
Unless you’re in Tibet, Xinjiang, or another place observing UTC+8 with a significant offset from local solar time.
rRNA: typical. I do the work and everyone else takes credit.
There’s even an instructables on how to do it.
I wonder if there will be an anti SLAPP action soon from Cohen’s team. (Not sure what the rules are in that jurisdiction.)
Exactly.
The general approach is to use interpretable models where you can understand how the model works and what features it uses to discriminate, but that doesn’t work for all ML approaches (and even when it does our understanding is incomplete.)