Contents
Description of demo_multiclass_CV.m
Demonstrates usage of ml_general_CV on model parameters for multiclass classification using multinomial logistic regression under RBF basis
clear all close all generateData_gridMulti
usage of RBF basis multi-class logistic regression
options_bs = [];
options_bs.subModel = @ml_multiclass_logistic;
options_bs.subOptions.addBias = 1;
options_bs.subOptions.lambdaL2 = 1e-8;
options_bs.basisFunc = @ml_kernel_rbf;
options_bs.basisOptions.sigma = 2;
model_bs = ml_multiclass_basis(Xtrain, ytrain, options_bs);
yhat_bs = model_bs.predict(model_bs, Xtest);
testError_bs = mean(yhat_bs ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_bs.name, testError_bs);
figure;
plotClassifier(Xtrain, ytrain, model_bs);
Averaged misclassification test error with Classification under Basis Change with: RBF Kernel is: 0.289
usage of RBF basis multi-class logistic regression with CV on sigma
options_cv1 = []; options_cv1.subModel = @ml_multiclass_basis; options_cv1.subOptions.subModel = @ml_multiclass_logistic; options_cv1.subOptions.subOptions.addBias = 1; options_cv1.subOptions.subOptions.lambdaL2 = 8; options_cv1.subOptions.basisFunc = @ml_kernel_rbf; options_cv1.paramNames = 'basisOptions.sigma'; options_cv1.paramValues = [0.5 1 1.5 2 2.5]'; options_cv1.nParams = 1; options_cv1.loss = 'mc'; model_cv1 = ml_general_CV(Xtrain, ytrain, options_cv1); yhat_cv1 = model_cv1.predict(model_cv1, Xtest); testError_cv1 = mean(yhat_cv1 ~= ytest); fprintf('Averaged misclassification test error with %s is: %.3f\n', model_cv1.name, testError_cv1); fprintf('Best %s is: %.3f\n', options_cv1.paramNames, model_cv1.bestParams); figure; plotClassifier(Xtrain, ytrain, model_cv1);
Averaged misclassification test error with CV on: basisOptions.sigma is: 0.164 Best basisOptions.sigma is: 1.000
usage of RBF basis multi-class logistic regression with CV on sigma and lambda (L2 regularizer)
options_cv2 = [];
options_cv2.subModel = @ml_multiclass_basis;
options_cv2.subOptions.subModel = @ml_multiclass_logistic;
options_cv2.subOptions.subOptions.addBias = 1;
options_cv2.subOptions.basisFunc = @ml_kernel_rbf;
options_cv2.paramNames = {'basisOptions.sigma', 'subOptions.lambdaL2'};
options_cv2.paramValues = {[0.5 1 1.5 2 2.5]',[0.5 1 1.5 2 2.5]'};
options_cv2.nParams = 2;
options_cv2.loss = 'mc';
model_cv2 = ml_general_CV(Xtrain, ytrain, options_cv2);
yhat_cv2 = model_cv2.predict(model_cv2, Xtest);
testError_cv2 = mean(yhat_cv2 ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_cv2.name, testError_cv2);
fprintf('Best %s is: %.3f\n', options_cv2.paramNames{1}, model_cv2.bestParams(1));
fprintf('Best %s is: %.3f\n', options_cv2.paramNames{2}, model_cv2.bestParams(2));
figure;
plotClassifier(Xtrain, ytrain, model_cv2);
Averaged misclassification test error with CV on: basisOptions.sigma & subOptions.lambdaL2 is: 0.218 Best basisOptions.sigma is: 0.500 Best subOptions.lambdaL2 is: 1.000
