我需要跟踪 F1 分数，同时在 SVM 中调整 C ＆ amp;Sigma，例如的代码跟踪准确性，我需要将其更改为 F1-Score，但我无法做到这一点......

%# read some training data
[labels,data] = libsvmread('./heart_scale');
%# grid of parameters
folds = 5;
[C,gamma] = meshgrid(-5:2:15, -15:2:3);
%# grid search, and cross-validation
cv_acc = zeros(numel(C),1);
    for i=1:numel(C)
cv_acc(i) = svmtrain(labels, data, ...
                sprintf('-c %f -g %f -v %d', 2^C(i), 2^gamma(i), folds));
end
%# pair (C,gamma) with best accuracy
[~,idx] = max(cv_acc);
%# now you can train you model using best_C and best_gamma
best_C = 2^C(idx);
best_gamma = 2^gamma(idx);
%# ...

我看到了以下两个链接

我明白,我必须首先在训练数据上找到最佳的 C 和 gamma / sigma 参数,然后使用这两个值做一个 LEE-ONE-OUT 交叉验证分类实验,所以我现在想要的是首先做一个网格搜索来调整 C & amp;sigma。请我更喜欢使用 MATLAB-SVM 而不是 LIBSVM。是我的 LEE-ONE-OUT 交叉验证分类代码。

... clc
 clear all
close all
a = load('V1.csv');
X = double(a(:,1:12));
Y = double(a(:,13));
% train data
datall=[X,Y];
A=datall;
n = 40;
ordering = randperm(n);
B = A(ordering, :);  
good=B; 
input=good(:,1:12);
target=good(:,13);
CVO = cvpartition(target,'leaveout',1);  
cp = classperf(target);                      %# init performance tracker
svmModel=[];
for i = 1:CVO.NumTestSets                                %# for each fold
trIdx = CVO.training(i);              
teIdx = CVO.test(i);                   
%# train an SVM model over training instances
svmModel = svmtrain(input(trIdx,:), target(trIdx), ...
       'Autoscale',true, 'Showplot',false, 'Method','ls', ...
      'BoxConstraint',0.1, 'Kernel_Function','rbf', 'RBF_Sigma',0.1);
%# test using test instances
pred = svmclassify(svmModel, input(teIdx,:), 'Showplot',false);
%# evaluate and update performance object
cp = classperf(cp, pred, teIdx); 
end
%# get accuracy
accuracy=cp.CorrectRate*100
sensitivity=cp.Sensitivity*100
specificity=cp.Specificity*100
PPV=cp.PositivePredictiveValue*100
NPV=cp.NegativePredictiveValue*100
%# get confusion matrix
%# columns:actual, rows:predicted, last-row: unclassified instances
cp.CountingMatrix
recallP = sensitivity;
recallN = specificity;
precisionP = PPV;
precisionN = NPV;
f1P = 2*((precisionP*recallP)/(precisionP + recallP));
f1N = 2*((precisionN*recallN)/(precisionN + recallN));
aF1 = ((f1P+f1N)/2);

我已经改变了 code，但我犯了一些错误，我得到的错误，

a = load('V1.csv');
X = double(a(:,1:12));
Y = double(a(:,13));
% train data
datall=[X,Y];
A=datall;
n = 40;
ordering = randperm(n);
B = A(ordering, :);  
good=B; 
inpt=good(:,1:12);
target=good(:,13);
k=10;
cvFolds = crossvalind('Kfold', target, k);   %# get indices of 10-fold CV
cp = classperf(target);                      %# init performance tracker
svmModel=[];
for i = 1:k 
    testIdx = (cvFolds == i);    %# get indices of test    instances
trainIdx = ~testIdx;   
C = 0.1:0.1:1; 
S = 0.1:0.1:1; 
fscores = zeros(numel(C), numel(S)); %// Pre-allocation
for c = 1:numel(C)   
for s = 1:numel(S)
    vals = crossval(@(XTRAIN, YTRAIN, XVAL, YVAL)(fun(XTRAIN, YTRAIN, XVAL, YVAL, C(c), S(c))),inpt(trainIdx,:),target(trainIdx));
    fscores(c,s) = mean(vals);
end
end
 end
[cbest, sbest] = find(fscores == max(fscores(:)));
C_final = C(cbest);
S_final = S(sbest);    

.......

功能……

.....
function fscore = fun(XTRAIN, YTRAIN, XVAL, YVAL, C, S)
svmModel = svmtrain(XTRAIN, YTRAIN, ...
   'Autoscale',true, 'Showplot',false, 'Method','ls', ...
  'BoxConstraint', C, 'Kernel_Function','rbf', 'RBF_Sigma', S);
   pred = svmclassify(svmModel, XVAL, 'Showplot',false);
   cp = classperf(YVAL, pred)
   %# get accuracy
    accuracy=cp.CorrectRate*100
    sensitivity=cp.Sensitivity*100
    specificity=cp.Specificity*100
    PPV=cp.PositivePredictiveValue*100
    NPV=cp.NegativePredictiveValue*100
    %# get confusion matrix
    %# columns:actual, rows:predicted, last-row: unclassified instances
    cp.CountingMatrix
    recallP = sensitivity;
    recallN = specificity;
    precisionP = PPV;
    precisionN = NPV;
    f1P = 2*((precisionP*recallP)/(precisionP + recallP));
    f1N = 2*((precisionN*recallN)/(precisionN + recallN));
    fscore = ((f1P+f1N)/2);
    end