Lets say that there's a binary classification problem where $X$$R_p$ and $Y ∈ \{0,1\} $ and $Pr(Y = 1 | X = x) = p$ for $p$ in $[0,1]$. There is a loss function $L_{falseneg} > 0$ for false prediction of Y = 0 when the outcome is Y = 1, and vice versa for $L_{falsepos}$. How would you find a threshold value so that expected loss criterion for making a prediction is equivalent to predicting $Y= 1$ if $p ≥ threshold$ and predicting $Y = 0$ otherwise.

I thought about approaching it using Neymay Pearson Test, but would there be a simpler way to do this?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.