how to specify the input and target data
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I have a dataset 2310x25 table. I dont know how to specify the input and target data. i'm using the below code for k fold cross validation.
data= dlmread('data\\inputs1.txt'); %inputs
groups=dlmread('data\\targets1.txt'); % target
Fold=10;
indices = crossvalind('Kfold',length(groups),Fold);
for i =1:Fold
testy = (indices == i);
trainy = (~testy);
TestInputData=data(testy,:)';
TrainInputData=data(trainy,:)';
TestOutputData=groups(testy,:)';
TrainOutputData=groups(trainy,:)';
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Walter Roberson
2022년 6월 21일
filename = 'https://www.mathworks.com/matlabcentral/answers/uploaded_files/1038775/bankruptcy.csv';
opt = detectImportOptions(filename, 'TrimNonNumeric', true);
data = readmatrix(filename, opt);
data = rmmissing(data);
groups = data(:,end);
data = data(:,1:end-1);
whos groups
[sum(groups==0), sum(groups==1)]
cp = classperf(groups);
Fold=10;
indices = crossvalind('Kfold',length(groups),Fold);
failures = 0;
for i =1:Fold
test = (indices == i);
train = ~test;
try
class = classify(data(test,:), data(train,:), groups(train,:));
classperf(cp, lass, test);
catch ME
failures = failures + 1;
if failures <= 5
fprintf('failed on iteration %d\n', i);
else
break
end
end
end
cp
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Walter Roberson
2022년 6월 21일
The reason for the failure is that you only have 30 entries with class 1, and when you are doing random selection for K-fold purposes, you are ending up with situations where there are no entries for class 1 in the training data.
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