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How to calculate ROC for repeated cross validation?
Use a proper accuracy score and accompany it with the c -index (concordance probability; AUROC) which is much easier to deal with than the curve, since it is calculated easily and quickly using the Wilcoxon-Mann-Whitney statistic. Thanks for contributing an answer to Cross Validated! Please be sure to answer the question.
How to calculate ROC curve with confidence interval?
To get a ROC curve you basically plot the true positive rate (TPR) against the false positive rate (FPR). To indicate the performance of your model you calculate the area under the ROC curve (AUC). Lets say we trained a XGBoost classifiers in a 100 x 5-folds cross validation and got 500 results.
How is the mean of the AUC calculated?
For each fold, the empirical AUC is calculated, and the mean of the fold AUCs is the cross-validated AUC estimate. The area under the ROC curve is equal to the probability that the classifier will score a randomly drawn positive sample higher than a randomly drawn negative sample.
How is the mean of the cvauc function calculated?
This function calculates cross-validated area under the ROC curve (AUC) esimates. For each fold, the empirical AUC is calculated, and the mean of the fold AUCs is the cross-validated AUC estimate.
How many repeats of 10-fold cross validation are needed?
Note that 100 repeats of 10-fold cross-validation may be required to achieve adequate precision. Or use the Efron-Gong optimism bootstrap which requires fewer iterations for the same precision (see e.g. R rms package validate functions).
How to use Receiver Operating Characteristic with cross validation?
Receiver Operating Characteristic (ROC) with cross validation ¶ Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality using cross-validation. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis.
How to calculate ROC curve per partition and resample?
The following example computes the ROC curve per partition and resample, so with 10 partitions and 5 repeats will result in 50 ROC curves: Depending on your data and model, the latter will give you certain variance in the resulting ROC curves and AUC values.