ReceiverOperatingCharacteristic Class

[SerializableAttribute]
public class ReceiverOperatingCharacteristic

<SerializableAttribute>
Public Class ReceiverOperatingCharacteristic

// This example shows how to measure the accuracy of a 
// binary classifier using a ROC curve. For this example,
// we will be creating a Support Vector Machine trained
// on the following training instances:

double[][] inputs =
{
    // Those are from class -1
    new double[] { 2, 4, 0 },
    new double[] { 5, 5, 1 },
    new double[] { 4, 5, 0 },
    new double[] { 2, 5, 5 },
    new double[] { 4, 5, 1 },
    new double[] { 4, 5, 0 },
    new double[] { 6, 2, 0 },
    new double[] { 4, 1, 0 },

    // Those are from class +1
    new double[] { 1, 4, 5 },
    new double[] { 7, 5, 1 },
    new double[] { 2, 6, 0 },
    new double[] { 7, 4, 7 },
    new double[] { 4, 5, 0 },
    new double[] { 6, 2, 9 },
    new double[] { 4, 1, 6 },
    new double[] { 7, 2, 9 },
};

int[] outputs =
{
    -1, -1, -1, -1, -1, -1, -1, -1, // fist eight from class -1
    +1, +1, +1, +1, +1, +1, +1, +1  // last eight from class +1
};

// Next, we create a linear Support Vector Machine with 4 inputs
SupportVectorMachine machine = new SupportVectorMachine(inputs: 3);

// Create the sequential minimal optimization learning algorithm
var smo = new SequentialMinimalOptimization(machine, inputs, outputs);

// We learn the machine
double error = smo.Run();

// And then extract its predicted labels
double[] predicted = new double[inputs.Length];
for (int i = 0; i < predicted.Length; i++)
    predicted[i] = machine.Compute(inputs[i]);

// At this point, the output vector contains the labels which
// should have been assigned by the machine, and the predicted
// vector contains the labels which have been actually assigned.

// Create a new ROC curve to assess the performance of the model
var roc = new ReceiverOperatingCharacteristic(outputs, predicted);
roc.Compute(100); // Compute a ROC curve with 100 cut-off points

// Generate a connected scatter plot for the ROC curve and show it on-screen
ScatterplotBox.Show(roc.GetScatterplot(includeRandom: true), nonBlocking: true)

    .SetSymbolSize(0)      // do not display data points
    .SetLinesVisible(true) // show lines connecting points
    .SetScaleTight(true)   // tighten the scale to points
    .WaitForClose();