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m →Expected loss: added [ ] and ( ) after the E for expected value since it was confusing without them, and I had to go to the article for MSE to be sure. Ideally I would have liked to add brackets under "Frequentist expected loss", but I got an error message when I tried, so I changed that back to how it was. |
Merge proposal closed (and I agree these should be kept separate) |
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In [[mathematical optimization]] and [[decision theory]], a '''loss function''' or '''cost function''' (sometimes also called an error function)<ref name="ttf2001">{{cite book|first1=Trevor |last1=Hastie |authorlink1= |first2=Robert |last2=Tibshirani |authorlink2=Robert Tibshirani|first3=Jerome H. |last3=Friedman |authorlink3=Jerome H. Friedman |title=The Elements of Statistical Learning |publisher=Springer |year=2001 |isbn=0-387-95284-5 |page=18 |url=https://web.stanford.edu/~hastie/ElemStatLearn/}}</ref> is a function that maps an [[event (probability theory)|event]] or values of one or more variables onto a [[real number]] intuitively representing some "cost" associated with the event. An [[optimization problem]] seeks to minimize a loss function. An '''objective function''' is either a loss function or its opposite (in specific domains, variously called a [[reward function]], a [[profit function]], a [[utility function]], a [[fitness function]], etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy.
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