## Contents

- Description of demo_multiclass_binarysubclassifiers.m
- usage of 1-vs-all algorithm on binary logistic classifier
- usage of 1-vs-1 algorithm on binary logistic classifier

## Description of demo_multiclass_binarysubclassifiers.m

Multiclass classification using binary classification subclassifiers with 1-vs-1 or 1-vs-all design

    clear all
    close all
    generateData_5grid

## usage of 1-vs-all algorithm on binary logistic classifier

    options_1va = [];
    options_1va.subModel = @ml_binaryclass_logistic;
    model_1va = ml_multiclass_1vA(Xtrain, ytrain, options_1va);
    yhat_1va = model_1va.predict(model_1va, Xtest);
    testError_1va = mean(yhat_1va ~= ytest);
    fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
            model_1va.name, testError_1va);

    Averaged misclassification test error with 1-vs-All Classification is: 0.147

## usage of 1-vs-1 algorithm on binary logistic classifier

    options_1v1 = [];
    options_1v1.subModel = @ml_binaryclass_logistic;
    model_1v1 = ml_multiclass_1v1(Xtrain, ytrain, options_1v1);
    yhat_1v1 = model_1v1.predict(model_1v1, Xtest);
    testError_1v1 = mean(yhat_1va ~= ytest);
    fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
            model_1v1.name, testError_1v1);

    Averaged misclassification test error with 1-vs-1 Classification is: 0.147

    figure;
    plotClassifier(Xtrain, ytrain, model_1va);
    figure;
    plotClassifier(Xtrain, ytrain, model_1v1);