how to specify the input and target data

Question

uma 2022-6-16

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1741625-how-to-specify-the-input-and-target-data

评论： Walter Roberson 2022-6-21

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 2022-6-20

Are you aware that some of the entries are question mark?

uma 2022-6-21

yes I know that. Now can you tell me how this dataset can be used to specify the input and target data

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Answer 1

Walter Roberson 2022-6-21

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1741625-how-to-specify-the-input-and-target-data#answer_989965

在 MATLAB Online 中打开

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
  Name           Size            Bytes  Class     Attributes

  groups      3194x1             25552  double              
[sum(groups==0), sum(groups==1)]
ans = 1×2
        3164          30
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
failed on iteration 1
failed on iteration 2
failed on iteration 3
failed on iteration 4
failed on iteration 5
cp
                        Label: ''
                  Description: ''
                  ClassLabels: [2×1 double]
                  GroundTruth: [3194×1 double]
         NumberOfObservations: 3194
               ControlClasses: 2
                TargetClasses: 1
            ValidationCounter: 0
           SampleDistribution: [3194×1 double]
            ErrorDistribution: [3194×1 double]
    SampleDistributionByClass: [2×1 double]
     ErrorDistributionByClass: [2×1 double]
               CountingMatrix: [3×2 double]
                  CorrectRate: NaN
                    ErrorRate: NaN
              LastCorrectRate: 0
                LastErrorRate: 0
             InconclusiveRate: NaN
               ClassifiedRate: NaN
                  Sensitivity: NaN
                  Specificity: NaN
      PositivePredictiveValue: NaN
      NegativePredictiveValue: NaN
           PositiveLikelihood: NaN
           NegativeLikelihood: NaN
                   Prevalence: NaN
              DiagnosticTable: [2×2 double]

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Walter Roberson 2022-6-21

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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