3 ms·
Though I don't know for this one, a semi-common strategy is to use ML to efficiently search for "interesting" datapoints, and then verify with conventional tool
by Groxx 6y ago
Though I don't know for this one, a semi-common strategy is to use ML to efficiently search for "interesting" datapoints, and then verify with conventional tools.
In that sort of use, it doesn't really matter if it's hallucinating - it'll just produce a lot of false-positives and you'll go (╯°□°)╯︵ ┻━┻ and try something else instead.
- Retric 6y agoAnything generating a lot of possible ‘interesting’ datapoints is very tricky to statistically validate. Essentially you need to raise the threshold for statistical significance based on the number of possibilities being considered. That’s almost never done correctly, and often misused. https://en.wikipedia.org/wiki/Data_dredging https://en.wikipedia.org/wiki/Data_dredging