title: Modelling card game for machine learning
body: <p>I'm looking for some help modelling this machine learning problem.</p>
  
  <p>A hand consists of three rows (containing 3, 5, and 5 cards respectively). Your goal is to build a hand that scores the most points. You receive the cards in intervals called streets, five cards in the first street, and three in the next four streets (you must discard one of the cards in the final four streets). Cards can't be moved once you place them. <a href="http://getduel.com/faq/" rel="nofollow">More details on scoring</a>.</p>
  
  <p>My goal is to build a system that, given a set of streets, plays the hand similar to our best players. It seem pretty clear that I'll need to build a neural network for each street, using features based on the existing hand and the set of cards in the street. I've got plenty of data (streets, placements, and final score), but I'm a little unsure how to model the problem given that the possible outputs are unique on the set of cards (although there are less than 3^5 placements in the first street, and 3^3 after). I've previously only dealt with classification problems with fixed categories.</p>
  
  <p>Does anyone have an example of a similar problem or suggestions how to prepare the training data when you have unique outputs?</p>