According to the formula of R-squared below (wiki), since I have only one predicted target value for each of the N folds, R2 is zero unless the predicted value coincides exactly with the true target value. This seems to render the application of R2
useless in this case. Are there other metrics I may use with a similar straightforward interpretation? What other ways exist to approach this problem?
P.S.: One alternative I came up with is computing R2 using the set of all predicted target values (from all N folds) and the true target values. However, this leaves me without an estimation of its variance. Has it a paper to support this approach?
same problem with (https://stats.stackexchange.com/questions/405872/leave-one-out-cross-validation-in-regression-r-squared-cannot-be-used-how-els?rq=1)
Please look at the class
Validation
by professor
Yasser
from
Caltech university
on
YouTube
Dear Wang,
Any traditional error metric (e.g. root mean square error, mean absolute error, etc.) can be calculated on leave one out cross-validation data.
HTH,
Ákos
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