The phrase "interpreted as wrong" is correct and usable in written English.
This phrase can be used to indicate that a particular idea, statement, or interpretation has been perceived to be incorrect. For example, "His comments were interpreted as wrong by some, but they were actually well-intended."
This situation could be interpreted as a "wrong clue" to the technique, as the negatively correlated construct would be assigning higher scores to files that, according to the rest of the constructs, should have lower scores.
It wasn't uncommon, at that time, for hypochondriacs – along with hysterics and melancholics – to be committed to asylums, their insistence that "something is wrong" interpreted as proof of their insanity.
"Even a small change is interpreted as meaning that expropriation was wrong," said Andrés Antonius, a former undersecretary for energy policy who now works as director of strategic planning at the Autonomous Technological Institute of Mexico.
If war did not come next week, the wince would be interpreted as his tut-tutting at the wrong many who could only guess, and badly, the future".
Insurance experts said State Farm's willingness to engage in settlement talks should not be interpreted as a concession that it did anything wrong.
This might be interpreted as a poor excuse, but it is also wrong.
Tom Burke, who advised Michael Heseltine, John Gummer and Michael Howard on the environment, said: "The leadership are getting the politics badly wrong, as the change is being interpreted as the return of the nasty party.
As Forbes put it, "If you find yourself rushing to take down an ad that could be interpreted as making light of rape, you're doing internet marketing wrong".
For a sub-population that has historically had its actions ruthlessly regulated and the meanings of right and wrong defined for them, this might be interpreted as an impressive act of defiance.
The AUC can be interpreted as being equal to the probability that LS ¯ of the wrong model exceeds that of the true NB model, i.e. the probability that the wrong model has a lower predictive performance compared with the true data generating distribution.
