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.
This is why the incremental cost of a unit are often a better measure for longer term profitability and decision making than the unit average cost, especially when you aren’t factoring in the market size and ability to repurpose sunk costs in that unit average cost.
Unless you’re in Tibet, Xinjiang, or another place observing UTC+8 with a significant offset from local solar time.
There’s even an instructables on how to do it.
They also make NEMA plugs with threaded rings around the boot for applications in marine and other harsh environments.
I’ve had great luck with Upton teas for the past decade or more in the US. Great selection, teas at multiple price points from broken leaf to first flush single estate.
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.)