www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/RefPlotconfusionExample.m
%% Plot Confusion Matrix % This example shows how to train a pattern recognition network and plot its accuracy. %% % Load the sample data. [x,t] = cancer_dataset; %% % |cancerInputs| is a 9x699 matrix defining nine attributes of 699 % biopsies. |cancerTargets| is a 2x966 matrix where each column indicates a % correct category with a one in either element 1 (benign) or element 2 % (malignant). For more information on this dataset, type |help % cancer_dataset| in the command line. %% % Create a pattern recognition network and train it using the sample data. net = patternnet(10); net = train(net,x,t); %% % Estimate the cancer status using the trained network, |net| . y = net(x); %% % Plot the confusion matrix. plotconfusion(t,y) %% % In this figure, the first two diagonal cells show the number and % percentage of correct classifications by the trained network. For example % 446 biopsies are correctly classifed as benign. This corresponds to % 63.8% of all 699 biopsies. Similarly, 236 cases are % correctly classified as malignant. This corresponds to 33.8% of all biopsies. % % 5 of the malignant biopsies are incorrectly classified as benign and this % corresponds to 0.7% of all 699 biopsies in the data. Similarly, 12 of the % benign biopsies are incorrectly classified as malignant and this % corresponds to 1.7% of all data. % % Out of 451 benign predictions, 98.9% are correct and 1.1% are wrong. Out % of 248 malignant predictions, 95.2% are correct and 4.8% are wrong. Out % of 458 benign cases, 97.4% are correctly predicted as benign and % 2.6% are predicted as malignant. Out of 241 malignant cases, 97.9% % are correctly classified as malignant and 2.1% are % classified as benign. % % Overall, 97.6% of the predictions are correct and 2.4% are wrong % classifications.