A Naive-Bayes classier requires estimation of the conditional
probabilities for each attribute value given the
label. For discrete data, because only few parameters
need to be estimated, the estimates tend to stabilize
quickly and more data does not change the underlying
model much. With continuous attributes, the discretization
is likely to form more intervals as more data
is available, thus increasing the representation power.
However, even with continuous data, the discretization
is global and cannot take into account attribute interactions.