使用libsvm进行交叉验证后的再培训 [英] Retraining after Cross Validation with libsvm

问题描述

我知道交叉验证用于选择良好的参数.找到它们之后，我需要在不使用-v选项的情况下重新训练整个数据.

I know that Cross validation is used for selecting good parameters. After finding them, i need to re-train the whole data without the -v option.

但是我面临的问题是，在使用-v选项进行训练之后，我得到了交叉验证的准确性(例如85％).没有模型，我看不到C和gamma的值.在那种情况下，我该如何重新训练?

But the problem i face is that after i train with -v option, i get the cross-validation accuracy( e.g 85%). There is no model and i can't see the values of C and gamma. In that case how do i retrain?

我将进行10倍交叉验证. 例如

Btw i applying 10 fold cross validation. e.g

optimization finished, #iter = 138
nu = 0.612233
obj = -90.291046, rho = -0.367013
nSV = 165, nBSV = 128
Total nSV = 165
Cross Validation Accuracy = 98.1273%

需要一些帮助.

为了获得最佳的C和伽玛，我使用LIBSVM常见问题解答中提供的代码

To get the best C and gamma, i use this code that is available in the LIBSVM FAQ

bestcv = 0;
for log2c = -6:10,
  for log2g = -6:3,
    cmd = ['-v 5 -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];
    cv = svmtrain(TrainLabel,TrainVec, cmd);
    if (cv >= bestcv),
      bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
    end
    fprintf('(best c=%g, g=%g, rate=%g)\n',bestc, bestg, bestcv);
  end
end

另一个问题:使用-v选项后的交叉验证准确性是否类似于我们在不使用-v选项进行训练并使用该模型进行预测时得到的交叉验证准确性?两种精度是否相似?

Another question : Is that cross-validation accuracy after using -v option similar to that we get when we train without -v option and use that model to predict? are the two accuracy similar?

另一个问题:交叉验证通过避免过度拟合从根本上提高了模型的准确性.因此，它需要先有一个模型，然后才能进行改进.我对吗?除此之外，如果我使用不同的模型，那么交叉验证的准确性会有所不同吗?我说的对吗?

Another question : Cross-validation basically improves the accuracy of the model by avoiding the overfitting. So, it needs to have a model in place before it can improve. Am i right? Besides that, if i have a different model, then the cross-validation accuracy will be different? Am i right?

另一个问题:在交叉验证准确性中，C和gamma的值是多少?

One more question: In the cross-validation accuracy, what is the value of C and gamma then?

图是这样的

然后，C的值为2，伽玛= 0.0078125.但是当我用新参数重新训练模型时.该值与99.63％不同.可能有什么原因吗? 预先感谢...

Then the values of C are 2 and gamma = 0.0078125. But when i retrain the model with the new parameters. The value is not the same as 99.63%. Could there be any reason? Thanks in advance...

推荐答案

此处的-v选项实际上旨在用作避免过拟合问题的方法(而不是使用整个数据进行训练，而是执行N -在N-1折叠上进行交叉交叉验证训练，并在其余折叠上进行测试(一次一次，然后报告平均准确度).因此，它仅将交叉验证的准确性(假设您有分类问题，否则要进行回归的均方误差)作为标量数而不是实际的SVM模型返回.

The -v option here is really meant to be used as a way to avoid the overfitting problem (instead of using the whole data for training, perform an N-fold cross-validation training on N-1 folds and testing on the remaining fold, one at-a-time, then report the average accuracy). Thus it only returns the cross-validation accuracy (assuming you have a classification problem, otherwise mean-squared error for regression) as a scalar number instead of an actual SVM model.

如果要执行模型选择，则必须使用交叉验证(类似于grid.py辅助python脚本)实施网格搜索，以找到C的最佳值和gamma.

If you want to perform model selection, you have to implement a grid search using cross-validation (similar to the grid.py helper python script), to find the best values of C and gamma.

这不难实现:使用MESHGRID创建值网格，迭代所有对对(C,gamma)训练具有5倍交叉验证的SVM模型，并选择具有最佳CV精度的值...

This shouldn't be hard to implement: create a grid of values using MESHGRID, iterate overall all pairs (C,gamma) training an SVM model with say 5-fold cross-validation, and choosing the values with the best CV-accuracy...

示例:

%# 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);

%# contour plot of paramter selection
contour(C, gamma, reshape(cv_acc,size(C))), colorbar
hold on
plot(C(idx), gamma(idx), 'rx')
text(C(idx), gamma(idx), sprintf('Acc = %.2f %%',cv_acc(idx)), ...
    'HorizontalAlign','left', 'VerticalAlign','top')
hold off
xlabel('log_2(C)'), ylabel('log_2(\gamma)'), title('Cross-Validation Accuracy')

%# now you can train you model using best_C and best_gamma
best_C = 2^C(idx);
best_gamma = 2^gamma(idx);
%# ...

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