What Random Forests Tell Us About Democracy

A popular method for learning from large data sets is Random Forests (see my class on the topic, in Spanish). I would like to drive a paralellism between the way they work and our political decision structures and the so called Wisdom of the crowd. Random Forests are what is called an ensemble method as they perform better than individual methods by combining their results. The individual method used in Random Forests are Decision Trees, trained from a subset of all the available data (and because of this property of operating on subsets of the data, they are a good method for applying on large datasets). More interestingly, Random Forests (as discussed in the Machine Learning article by Leo Breiman in 2001), can not only train each of their trees on a subset of the data but also use a subset of the available information (features) when training each decision node in the tree.