Partitioning may reverse the correlation or it may not; either way, it provides a more accurate model.
Usually. But, partitioning reduces the number of samples within each partition, and can thus increase the effects of chance. This is even worse if you have a lot of variables floating around that you can partition against. At some point it becomes easy to choose a partition that purely by coincidence is apparently very predictive on this data set, but that actually has no causal role.
RobinZ is that P(R|G,T) might overfit the data: the accuracy improvement achieved by including G might not justify the increase in model complexity.
Usually. But, partitioning reduces the number of samples within each partition, and can thus increase the effects of chance. This is even worse if you have a lot of variables floating around that you can partition against. At some point it becomes easy to choose a partition that purely by coincidence is apparently very predictive on this data set, but that actually has no causal role.
Exactly.