Following is the learning code:

// Specify the input variables
DecisionVariable[] variables =
{
new DecisionVariable("x", DecisionVariableKind.Continuous),
new DecisionVariable("y", DecisionVariableKind.Continuous),
};
// Create the C4.5 learning algorithm
var c45 = new C45Learning(variables);
// Learn the decision tree using C4.5
tree = c45.Learn(inputs, outputs);
// Show the learned tree in the view
decisionTreeView1.TreeSource = tree;
// Get the ranges for each variable (X and Y)
DoubleRange[] ranges = table.GetRange(0);
// Generate a Cartesian coordinate system
double[][] map = Matrix.Mesh(ranges[0], 
  200, ranges[1], 200);
// Classify each point in the Cartesian coordinate system
double[,] surface = map.ToMatrix().
  InsertColumn(tree.Decide(map));
CreateScatterplot(zedGraphControl2, surface);
//Testing
// Creates a matrix from the entire source data table
double[][] table = (dgvLearningSource.DataSource as
  DataTable).ToJagged(out columnNames);
// Get only the input vector values (first two columns)
double[][] inputs = table.GetColumns(0, 1);
// Get the expected output labels (last column)
int[] expected = table.GetColumn(2).ToInt32();
// Compute the actual tree outputs and turn 
  a Boolean into a 0 or 1
int[] actual = tree.Decide(inputs);
// Use confusion matrix to compute some statistics.
ConfusionMatrix confusionMatrix = 
  new ConfusionMatrix(actual, expected, 1, 0);
dgvPerformance.DataSource = new [] { confusionMatrix };
// Create performance scatter plot
CreateResultScatterplot(zedGraphControl1,
  inputs, expected.ToDouble(), actual.ToDouble());
// As you can see above, the tree is making 
  the decision via the code line
int[] actual = tree.Decide(inputs);

This value is then fed to a ConfusionMatrix. For those of you that are not familiar with this, let me briefly explain.