compsciclass

this is decision tree ID3 algorithm, this algorithm is one of decision tree algorithm like cart, cha

/*
  Data from Dr. Greiner's Cmput 466/551 class, Winter 2001
*/

CONCLUSION = 

  "Grade"      = { '7','8','9' } 
 
FEATURES = 

  "Grad/Ungrad"			= { 'ugrad','grad' },

  "Lisp"  			= { 'yes','no' },
  "Prolog"  			= { 'yes','no' },
  "Java"  			= { 'yes','no' },
  "C/C++"  			= { 'yes','no' },
  "VisualBasic"  		= { 'yes','no' },
	
  "Combinatorial explosion"	= { 'kw','v','n' },
  "Dynamic programming"		= { 'kw','v','n' },
  "Graph"			= { 'kw','v','n' },
  "NP-hard"			= { 'kw','v','n' },
  "Hill-climbing"		= { 'kw','v','n' },
  "O(f(n))"			= { 'kw','v','n' },
  "Heuristic"			= { 'kw','v','n' },
  "HOrn clauses"		= { 'kw','v','n' },

  "Bayes rule"			= { 'kw','v','n' },
  "Bayesian network"		= { 'kw','v','n' },
  "Independence"		= { 'kw','v','n' },
  "Influence"			= { 'kw','v','n' },
  "Information"			= { 'kw','v','n' },
  "Markov property"		= { 'kw','v','n' },
  "POMDP"			= { 'kw','v','n' },
  "Utility"			= { 'kw','v','n' },

  "Backpropagation"		= { 'kw','v','n' },
  "Bias/Variance Tradeoff"	= { 'kw','v','n' },
  "Clustering"			= { 'kw','v','n' },
  "Decision tree"		= { 'kw','v','n' },
  "Genetic algorithm"		= { 'kw','v','n' },
  "Gradient descent"		= { 'kw','v','n' },
  "Hypothesis testing"		= { 'kw','v','n' },
  "Learning rate"		= { 'kw','v','n' },
  "Neural net"			= { 'kw','v','n' },
  "Overfitting"			= { 'kw','v','n' },
  "PAC learning"		= { 'kw','v','n' },
  "Perceptron"			= { 'kw','v','n' },
  "Q learning"			= { 'kw','v','n' },
  "TD learning"			= { 'kw','v','n' }

TRAINDATA = "CompSciClass.data"