Pointwise loo likelihood for binary classification

loo
#1

Hi all,
Currently I am using stan for a binary classification problem. To check the discriminatory power of my model I want to use measures like the recall, precision and the receiver operator characteristics curve. Due to the size of the data set I do not want to split up my data set into a training and a validation set. Instead of this I thought about using a similar approach as used in Vehtari, Gelman, Gabry’s paper , in which pareto smoothed importance sampling is used the calculate the cross validation likelihood by:

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Where w_i^s is the weight determined by the pareto smoothing of raw importance sampling ratios.
As input for the recall etc., I want to use the cross validation likelihoods, however I am not sure if this is a good approach due to variance and bias in the approximation. Does anyone know if it is okay to use these approximations for these types of measures?
Kind regards,
Koen

#2

Maybe this example helps?

#3

Yes, thank you, I wanted to use something similar to subsection 4.3. I also like the addition of the qplot. Would you expect a high k-value when the a data point is not on the diagonal line?

#4

If by a data point you mean predictive probability vs loo predictive probability, then yes when their difference is large it’s more likely that corresponding khat is large.