How to plot confusion matrix for 2 classes (genuine or fraud)

clc

clear

trainingSetup = load("C:\Users\User\Desktop\Master Application\02 Dissertion\Signature\Kaggle\Real_A1\trainingSetup_2021_04_01__20_23_12.mat");

imdsTrain = imageDatastore("C:\Users\User\Desktop\Master Application\02 Dissertion\Signature\Testing","IncludeSubfolders",true,"LabelSource","foldernames");

[imdsTrain, imdsValidation] = splitEachLabel(imdsTrain,0.7,"randomized");

% Resize the images to match the network input layer.

augimdsTrain = augmentedImageDatastore([224 224 3],imdsTrain);

augimdsValidation = augmentedImageDatastore([224 224 3],imdsValidation);

opts = trainingOptions("rmsprop",...

"ExecutionEnvironment","auto",...

"InitialLearnRate",0.001,...

"Shuffle","every-epoch",...

"Plots","training-progress",...

"ValidationData",augimdsValidation);

layers = [

imageInputLayer([224 224 3],"Name","imageinput")

convolution2dLayer([3 3],32,"Name","conv","Padding","same")

reluLayer("Name","relu")

maxPooling2dLayer([5 5],"Name","maxpool","Padding","same")

fullyConnectedLayer(2,"Name","fc")

softmaxLayer("Name","softmax")

classificationLayer("Name","classoutput")];

[net, traininfo] = trainNetwork(augimdsTrain,layers,opts);

Answer 1

[Athul Prakash]

Hi Tam,

First, you may set aside some of your data as test data.

With this test dataset, obtain Y_Actual as the labels and X as the values in the test data.

After that,

YPred = predict(net, X);
cmat = confusionmat(YActual, YPred);

1) Check out the doc on confusionmat and the examples found there.

https://in.mathworks.com/help/stats/confusionmat.html

2) You may also refer to confusionchart(), which creates a plot of the confusion matrix as well.

https://in.mathworks.com/help/deeplearning/ref/confusionchart.html

Hope it helps!