Contents
Description of demo_multiclass_baggedDecisionTrees.m
Demonstrates bagged decision trees versus stump and decision trees for a multiclass classification task.
clear all close all generateData_4grid
stump classification baseline
options_st = []; options_st.error = 'err'; model_st = ml_multiclass_stump(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);
Averaged misclassification test error with Stump Classification is: 0.533
decision tree classification baseline
options_dt = [];
model_dt = ml_multiclass_decisionTree(Xtrain, ytrain, options_dt);
yhat_dt = model_dt.predict(model_dt, Xtest);
testError_dt = mean(yhat_dt ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_dt.name, testError_dt);
Averaged misclassification test error with Decision Tree is: 0.222
decision tree classification with bagging
options_bg = []; options_bg.nModels = 15; options_bg.subModel = @ml_multiclass_decisionTree; model_bg = ml_multiclass_bagging(Xtrain, ytrain, options_bg); yhat_bg = model_bg.predict(model_bg, Xtest); testError_bg = mean(yhat_bg ~= ytest); fprintf('Averaged misclassification test error with %s is: %.3f\n',... model_bg.name, testError_bg)
Averaged misclassification test error with Classification with Bagging is: 0.138
figure; plotClassifier(Xtrain, ytrain, model_st); figure; plotClassifier(Xtrain, ytrain, model_dt); figure; plotClassifier(Xtrain, ytrain, model_bg);


