title: Machine Learning - Which approach fits best to predict discrete,continuous solutions from features of various importance?
body: <p>I need to come up with a formula that takes up to N inputs and calculates a single number that should predict the "correct" answer as often as possible. Each input is a decimal value or integer. The output is also a decimal value.</p>
  
  <p>I have an absurd amount of data (consider it infinite). In each case I have the value of all of the inputs and the correct value of the output.</p>
  
  <p>The features are all inter-related (i.e. when one is high another is more likely to be low) and they have differing degrees of relevance to the answer.</p>
  
  <p>There is no "perfect" formula but there should be one that holds true in many statistically significant cases. If I had a "best" solution it would likely be both non-linear and discrete. This is, however, a one-time computation.</p>
  
  <p>What machine learning solution is best for taking these kinds of features and creating an accurate-ish model to represent such complicated, somewhat-random data?</p>
  
  <p>Edit: Doing some more research it appears that any kind of linear regression will fail. Neural networks look like the best choice but I don't know if they can predict this "discrete" function that has different formulas in different ranges.</p>