I'm exploring regression trees (using Rattle) for the prediction of various variables in the natural sciences. If my target variable has distinctly different 'modes' or patterns of behaviour, will the models benefit from (or in fact even require) that I partition my target variable (and perhaps model each mode separately)?Some examples to explain what I mean -Climatic phenomena, which might have very different properties (mean, variability, trend) and drivers (predictors) in winter versus summer; Animal behaviour, which could be markedly different in mating seasoncompared to other times;  Consumer behaviour, which might be different during a recessionduring other times.In each case, should each distinct 'mode' be modelled separately? Or, if not separately, should the model include a variable to 'help' the model partition (for example, include a seasonal variable, a mating season variable, a GDP-based recession indicator)?One particular scenario that I'm curious about is if there is no relationship in some circumstances (for example, what if the climatic variable is only predictable - from the set of chosen IV - during winter ... will including summer variables 'confuse' the results)?
Do I need to partition my target (DV) variable?
predictive modeling;decision trees