public static void decisionTreeDemo() { try { DataSet ds = DataSetFactory.getRestaurantDataSet(); DecisionTreeLearner learner = new DecisionTreeLearner(); learner.Train(ds); System.Console.WriteLine("The Induced Decision Tree is "); System.Console.WriteLine(learner.getDecisionTree()); int[] result = learner.Test(ds); System.Console.WriteLine("\nThis Decision Tree classifies the data set with " + result[0] + " successes" + " and " + result[1] + " failures"); System.Console.WriteLine("\n"); } catch (Exception e) { System.Console.WriteLine("Decision Tree Demo Failed "); throw e; } }
public void testInducedDecisionTreeClassifiesRestaurantDataSetCorrectly() { DecisionTreeLearner learner = new DecisionTreeLearner( createInducedRestaurantDecisionTree(), "Unable to clasify"); int[] results = learner.Test(DataSetFactory.getRestaurantDataSet()); Assert.AreEqual(12, results[0]); Assert.AreEqual(0, results[1]); }
public void testInducedTreeClassifiesDataSetCorrectly() { DataSet ds = DataSetFactory.getRestaurantDataSet(); DecisionTreeLearner learner = new DecisionTreeLearner(); learner.Train(ds); int[] result = learner.Test(ds); Assert.AreEqual(12, result[0]); Assert.AreEqual(0, result[1]); }
public void testStumpPredictionForDataSet() { DataSet ds = DataSetFactory.getRestaurantDataSet(); ICollection <string> unmatchedValues = CollectionFactory.CreateQueue <string>(); unmatchedValues.Add(NO); DecisionTree tree = DecisionTree.getStumpFor(ds, "hungry", YES, YES, unmatchedValues, "Unable to Classify"); DecisionTreeLearner learner = new DecisionTreeLearner(tree, "Unable to Classify"); int[] result = learner.Test(ds); Assert.AreEqual(5, result[0]); Assert.AreEqual(7, result[1]); }