## Contents

- Description of demo_multilabel_logistics.m
- usage of independent logistic regression
- usage of independent logistic regression with L2-regularization

## Description of demo_multilabel_logistics.m

Demonstrates use of independent logistic regression classifiers for each candidate class for multilabel classification

    clear all
    close all
    generateData_multiLabel

## usage of independent logistic regression

    options = struct('nLabels',nLabels);
    model = ml_multilabel_independent(Xtrain,ytrain,options);
    yhatTest = model.predict(model, Xtest);
    yhatTrain = model.predict(model, Xtrain);
    testError = sum(ytest~=yhatTest)/length(ytest);
    model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
    fprintf('Averaged misclassification test error with %s is: %.3f\n',...
            model.name, testError);
    linear_makeOneContourPlot(Xtrain,ytrain, model)

    Averaged misclassification test error with Independent Logistic Classifiers is: 0.010

## usage of independent logistic regression with L2-regularization

    options = struct('nLabels',nLabels,'lambdaL2',1e-4);
    model = ml_multilabel_independent(Xtrain,ytrain,options);
    yhatTest = model.predict(model, Xtest);
    yhatTrain = model.predict(model, Xtrain);
    testError = sum(ytest~=yhatTest)/length(ytest);
    model.trainError = sum(ytrain~=yhatTrain)/length(ytrain);
    fprintf('Averaged misclassification test error with %s is: %.3f\n',...
            model.name, testError);
    linear_makeOneContourPlot(Xtrain,ytrain, model)

    Averaged misclassification test error with Independent Logistic Classifiers is: 0.020