az.loo uses the PSIS approximation to estimate loo-cv results. However, this approximation can’t always be used. Even if the conditions for applying the approximation are met, if the model is very flexible, there are a lot of influential observations… the approximation itself fails as you get a lot pareto k values over 0.7. It looks like you are in this case. Increasing the number of posterior samples can help a bit with that, but it generally is more for a handful of bad points. In your case you’ll need to use a different approximation like importance weighted moment matching (not available in ArviZ yet) or resort to brute force cross-validation.