Contents
Description of demo_multiclass_MLP.m
Comparison of multiclass classification using multiclass logistic regression and multi-layer perceptron algorithms
clear all close all generateData_5grid
usage of multi-class logistic classification
options_lg = [];
options_lg.addBias = 1;
model_lg = ml_multiclass_logistic(Xtrain, ytrain, options_lg);
yhat_lg = model_lg.predict(model_lg, Xtest);
testError_lg = mean(yhat_lg ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_lg.name, testError_lg);
Averaged misclassification test error with Multiclass Logistic Classification is: 0.062
usage of MLP classification
options_mlp = [];
options_mlp.nHidden = [3 3 5];
model_mlp = ml_multiclass_MLP(Xtrain, ytrain, options_mlp);
yhat_mlp = model_mlp.predict(model_mlp, Xtest);
testError_mlp = mean(abs(yhat_mlp ~= ytest));
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_mlp.name, testError_mlp);
Averaged misclassification test error with Multi-layer Perceptron Classification is: 0.084
figure; plotClassifier(Xtrain, ytrain, model_lg); figure; plotClassifier(Xtrain, ytrain, model_mlp); generateData_gridMulti
usage of multi-class logistic classification
options_lg = [];
options_lg.addBias = 1;
model_lg = ml_multiclass_logistic(Xtrain, ytrain, options_lg);
yhat_lg = model_lg.predict(model_lg, Xtest);
testError_lg = mean(yhat_lg ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_lg.name, testError_lg);
Averaged misclassification test error with Multiclass Logistic Classification is: 0.289
usage of MLP classification
options_mlp = [];
options_mlp.nHidden = [5 5 5 5];
model_mlp = ml_multiclass_MLP(Xtrain, ytrain, options_mlp);
yhat_mlp = model_mlp.predict(model_mlp, Xtest);
testError_mlp = mean(yhat_mlp ~= ytest);
fprintf('Averaged misclassification test error with %s is: %.3f\n', model_mlp.name, testError_mlp);
Averaged misclassification test error with Multi-layer Perceptron Classification is: 0.324
figure; plotClassifier(Xtrain, ytrain, model_lg); figure; plotClassifier(Xtrain, ytrain, model_mlp);
