Contents

Description of demo_multiclass_discrim.m

Fits a variety of discriminative classifiers to two datasets for a multiclass classification

clear all
close all
generateData_5grid

usage of GDA with naive Bayes models (5grid data)

options_nb = [];
options_nb.subModel = @ml_generative_NB;
model_nb = ml_multiclass_GDA(Xtrain, ytrain, options_nb);
yhat_nb = model_nb.predict(model_nb, Xtest);
testError_nb = mean(yhat_nb ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_nb.name, testError_nb);
figure;
plotClassifier(Xtrain, ytrain, model_nb);

Averaged misclassification test error with Discr. Classification: Generative Naive Bayes Model is: 0.089

usage of GDA with Gaussian models (5grid data)

options_gs.subModel = @ml_generative_Gaussian;
model_gs = ml_multiclass_GDA(Xtrain, ytrain, options_gs);
yhat_gs = model_gs.predict(model_gs, Xtest);
testError_gs = mean(yhat_gs ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_gs.name, testError_gs);
figure;
plotClassifier(Xtrain, ytrain, model_gs);

Averaged misclassification test error with Discr. Classification: Generative Gaussian Model is: 0.076

usage of GDA with Student-t models (5grid data)

options_st = [];
options_st.subModel = @ml_generative_student;
model_st = ml_multiclass_GDA(Xtrain, ytrain, options_st);
yhat_st = model_st.predict(model_st, Xtest);
testError_st = mean(yhat_st ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_st.name, testError_st);
figure;
plotClassifier(Xtrain, ytrain, model_st);

Averaged misclassification test error with Discr. Classification: Generative Student's t Model is: 0.227

usage of GDA with Gaussian mixture models (5grid data)

options_mg = [];
options_mg.subModel = @ml_generative_mixtureGaussian;
options_mg.subOptions.nMixtures = 2;
model_mg = ml_multiclass_GDA(Xtrain, ytrain, options_mg);
yhat_mg = model_mg.predict(model_mg, Xtest);
testError_mg = mean(yhat_mg ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_mg.name, testError_mg);
figure;
plotClassifier(Xtrain, ytrain, model_mg);

Averaged misclassification test error with Discr. Classification: Generative Gaussian Mixture Model is: 0.089

usage of GDA with kernel density estimation models (5grid data)

options_kde = [];
options_kde.subModel = @ml_generative_KDE;
model_kde = ml_multiclass_GDA(Xtrain, ytrain, options_kde);
yhat_kde = model_kde.predict(model_kde, Xtest);
testError_kde = mean(yhat_kde ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_kde.name, testError_kde);
figure;
plotClassifier(Xtrain, ytrain, model_kde);

Averaged misclassification test error with Discr. Classification: Generative Kernel Density Estimation Model is: 0.244

generateData_gridMulti

usage of GDA with naive Bayes models (gridMulti data)

options_nb = [];
options_nb.subModel = @ml_generative_NB;
model_nb = ml_multiclass_GDA(Xtrain, ytrain, options_nb);
yhat_nb = model_nb.predict(model_nb, Xtest);
testError_nb = mean(yhat_nb ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_nb.name, testError_nb);
figure;
plotClassifier(Xtrain, ytrain, model_nb);

Averaged misclassification test error with Discr. Classification: Generative Naive Bayes Model is: 0.222

usage of GDA with Gaussian models (gridMulti data)

options_gs = [];
options_gs.subModel = @ml_generative_Gaussian;
model_gs = ml_multiclass_GDA(Xtrain, ytrain, options_gs);
yhat_gs = model_gs.predict(model_gs, Xtest);
testError_gs = mean(yhat_gs ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_gs.name, testError_gs);
figure;
plotClassifier(Xtrain, ytrain, model_gs);

Averaged misclassification test error with Discr. Classification: Generative Gaussian Model is: 0.164

usage of GDA with Student-t models (gridMulti data)

options_st = [];
options_st.subModel = @ml_generative_student;
model_st = ml_multiclass_GDA(Xtrain, ytrain, options_st);
yhat_st = model_st.predict(model_st, Xtest);
testError_st = mean(yhat_st ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_st.name, testError_st);
figure;
plotClassifier(Xtrain, ytrain, model_st);

Averaged misclassification test error with Discr. Classification: Generative Student's t Model is: 0.444

usage of GDA with Gaussian mixture models (gridMulti data)

options_mg = [];
options_mg.subModel = @ml_generative_mixtureGaussian;
options_mg.subOptions.nMixtures = 2;
model_mg = ml_multiclass_GDA(Xtrain, ytrain, options_mg);
yhat_mg = model_mg.predict(model_mg, Xtest);
testError_mg = mean(yhat_mg ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_mg.name, testError_mg);
figure;
plotClassifier(Xtrain, ytrain, model_mg);

Averaged misclassification test error with Discr. Classification: Generative Gaussian Mixture Model is: 0.262

usage of GDA with kernel density estimation models (gridMulti data)

options_kde = [];
options_kde.subModel = @ml_generative_KDE;
model_kde = ml_multiclass_GDA(Xtrain, ytrain, options_kde);
yhat_kde = model_kde.predict(model_kde, Xtest);
testError_kde = mean(yhat_kde ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', ...
        model_kde.name, testError_kde);
figure;
plotClassifier(Xtrain, ytrain, model_kde);

Averaged misclassification test error with Discr. Classification: Generative Kernel Density Estimation Model is: 0.524

Published with MATLAB® R2015a