When comparing results obtained with different models in R, what should I look for to select the best one?
If I use for example the following 4 models applied to the same presence/absence sample taken from a species dataset, with the same variables:
Generalized linear model
Generalized additive models Classification
Artificial Neural Networks
Should I compare all methods by AIC, Kappa, or cross-validation?
Will I ever be certain of selecting the best model?
What happens if I compare those 4 models prediction with a Bayes factor? Can I compare them?