DecisionStump Class

[SerializableAttribute]
public class DecisionStump : BinaryClassifierBase<double[]>

<SerializableAttribute>
Public Class DecisionStump
	Inherits BinaryClassifierBase(Of Double())

// Let's say we want to classify the following 2-dimensional 
// data samples into 2 possible classes, either true or false:
double[][] inputs =
{
    new double[] {  10, 42 },
    new double[] { 162, 96 },
    new double[] { 125, 20 },
    new double[] {  96,  6 },
    new double[] {   2, 73 },
    new double[] {  52, 51 },
    new double[] {  71, 49 },
};

// And those are their associated class labels
bool[] outputs =
{
    false, false, true, true, false, false, true
};

// We can create an AdaBoost algorithm as:
var learner = new AdaBoost<DecisionStump>()
{
    Learner = (p) => new ThresholdLearning(),

    // Train until:
    MaxIterations = 5,
    Tolerance = 1e-3
};

// Now, we can use the Learn method to learn a boosted classifier
Boost<DecisionStump> classifier = learner.Learn(inputs, outputs);

// And we can test its performance using (error should be 0):
ConfusionMatrix cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);

double error = cm.Error;  // should be 0.0
double acc = cm.Accuracy; // should be 1.0
double kappa = cm.Kappa;  // should be 1.0

// And compute a decision for a single data point using:
bool y = classifier.Decide(inputs[0]); // result should false

// Let's say we want to classify the following 2-dimensional 
// data samples into 2 possible classes, either true or false:
double[][] inputs =
{
    new double[] {  10, 42 },
    new double[] { 162, 96 },
    new double[] { 125, 20 },
    new double[] {  96,  6 },
    new double[] {   2, 73 },
    new double[] {  52, 51 },
    new double[] {  71, 49 },
};

// And those are their associated class labels
bool[] outputs =
{
    false, false, true, true, false, false, true
};

// We create a learning algorithm as:
var teacher = new ThresholdLearning();

// Now, we can use the Learn method to learn a classifier:
DecisionStump classifier = teacher.Learn(inputs, outputs);

// Now, we can check how good it is using a confusion matrix:
var cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);

double error = cm.Error; // should be ~0.14

// We can also compute the model outputs for new samples using
bool y = classifier.Decide(new double[] { 71, 48 }); // should be false