I have satellite data that provides radiance which I use to compute the Flux (using surface and cloud info). Now using a regression method, I can develop a mathematical model directly relating radiance and flux and can be used to predict the flux for new radiance values.
Is it possible to do same using decision trees or regression trees? In a regression there is mathematical equation connecting dependent and independent variable? Using decision trees, how could you develop such a model?
Best Answer
In algorithmic modeling, as opposed to parametric modeling, there is no explicit equation relating input and output variables (see this paper by Breiman). The assumption is that the phenomenon being modeled is complex and unknown, and rather than imposing a formal model (which comes with a suite of assumptions and limitations), algos directly learn the links between predictors and predictand from the data. In the case of a single tree, this is not so much of an issue because the tree explains its predictions in a very visual and intuitive manner, but with ensemble of trees (Random Forests, Boosting), interpretability is definitely traded off for accuracy.