You generally don't know if you've reached a suitable maxima, which is why it is good to run a nondeterministic optimizer a few times (if computation power allows) and see if there are any reliable parameters form there.
There are also somewhat better-than-random strategies such as Bayesian optimization and particle swarm optimization that can help you to search more efficiently.
You're right. But it's already known that the actual dress is "blue and black" as someone found it on amazon. It's essentially the picture that is creating the ambiguity.
I skimmed the source and didn't see anything regarding leader election. Am I missing something or is this implementation encapsulating some of the underlying terminology/analogies traditionally used by Raft?
One common approach is to look for the elbow in the curve <metric> vs K (number of clusters). This is essentially finding the number of clusters after which the rate of information gained/variance explained/<metric> slows. I believe it's possible to binary search for this point if you can assume the curve is convex.
I think it is more accurate to say that data science isn't Kaggle. The process of taking a data set and fitting it to the most robust model is certainly machine learning.
Yeah, trg2 would need to put all the edge case rules towards the beginning. That, or just put the basic rules at the beginning and have separate conditionals at the end to handle the edge cases.
33kt/hr * 1.15mi/kt * 1.609km/mi ~= 61km/hr.
The weather app was right. Just different units.