Contents
Description of demo_binaryclass_exponential.m
Demonstrates two weak base classifiers that can be used in more complex iterative methods like bagging and boosting
clear all close all load data_exponential.mat
Broken Stump model
% Train broken stump model options = []; [model_stump] = ml_binaryclass_brokenStump(Xtrain,ytrain,options); % Test broken stump model yhat = model_stump.predict(model_stump,Xtest); % Measure test error testError = sum(yhat~=ytest)/length(ytest); fprintf('Averaged absolute test error with %s is: %.3f\n', ... model_stump.name, testError); % Plot model predictions plot2DClassifier(Xtrain,ytrain,model_stump);
Averaged absolute test error with Broken Stump Binary Classification is: 0.136

Exponential-Loss model
% Train exponential loss model options.addBias = 1; options.lambdaL2 = 1; [model_exp] = ml_binaryclass_exponential(Xtrain,ytrain,options); % Test exponential loss model yhat = model_exp.predict(model_exp,Xtest); % Measure test error testError = sum(yhat~=ytest)/length(ytest); fprintf('Averaged absolute test error with %s is: %.3f\n', ... model_exp.name, testError); % Plot model predictions plot2DClassifier(Xtrain,ytrain,model_exp);
Averaged absolute test error with Exponential Loss is: 0.128
