Leave-One-Out with ROC

6 次查看（过去 30 天）
Aaronne
Aaronne 2013-8-5
Hi smart guys,
May I ask how to create ROC for the leave-one-out cross-validation?
I create partition of the data as:
cvPartition = cvpartition(dataSize, 'leaveout');
However, when I take
model = ClassificationDiscriminant.fit(X, Y, 'DiscrimType', 'linear');
[predictL, predictS] = model.predict(X_Test);
performanceVec = classperf(Y_Teset, predictL);
[FPR, TPR ,~, AUC] = perfcurve(Y_Test, predictS(:,2), 1);
in which, `X, Y, X_Test, Y_Test` are obtained from `cvPartition`. Then there are errors:
Error using classperf (line 205)
Ground truth must have at least two classes.
Error using perfcurve (line 368)
Less than two classes are found in the array of true class labels.
Anyone can give an example of using leave one out cross validation and ROC analysis using Matlab? Thanks a lot.
A.

请先登录，再回答此问题。

采纳的回答

Ilya
Ilya 2013-8-6
编辑：Ilya 2013-8-6
Here is an example for cross-validating a discriminant model: http://www.mathworks.com/help/stats/discriminant-analysis.html#bs2r8ue. Here is a description of the crossval method: http://www.mathworks.com/help/stats/classificationdiscriminant.crossval.html. You can just replace 'kfold',5 with 'leaveout','on' in that example.

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Discriminant Analysis 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by