Support vector machines and the electoral college

The decision process of the electoral college can be seen as a machine learning problem where the input consists of a vector of length 51 describing the election outcome in 50 states and the District of Columbia and where the final result is given as an output scalar equal to either +1 or -1. Support vector machines are applied in this context to a set of simulated and historic US presidential elections. The decision surface can best be modelled by a separating hyperplane where the weights of the hyperplane represent a scaled level of importance for each of the 50 states and the District of Columbia. For 100 simulations of 100 elections, in which Democrats and Republicans had a 50% probability of winning each state, support vector machines appear to retrieve a large-state bias. For a historic dataset covering the last 22 U.S. presidential elections no large-state bias could be detected.