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How do you compare two AUCS?
1 Answer
- Calculate AUC of A vs.
- Create C_1, such that C_1 is a pair-wisely randomly shuffled list of scores from A and B.
- Measure AUC of C_1.
- Test if AUC of C_1 is better than AUC of A.
- Repeat step 2 to 4 n many times, but instead of C_1, use C_i where i ∈{2,3,…,n}.
- Then, p=damnn.
What is area under the curve in statistics?
The area under the curve is an integrated measurement of a measurable effect or phenomenon. It is used as a cumulative measurement of drug effect in pharmacokinetics and as a means to compare peaks in chromatography.
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.
Which is better, the ROC curve or the AUC?
This is not very realistic, but it does mean that a larger area under the curve (AUC) is usually better. The “steepness” of ROC curves is also important, since it is ideal to maximize the true positive rate while minimizing the false positive rate.
Where do I get my AUC score from?
In my situation I obtain the following AUC scores: You should pay attention to measures of your models’ performance on the test data set. I think that your question stems from confusion over the purpose of the training, validation and test sets.
Can you run k-fold cross validation more than once?
Once model is selected again use the same 10000 samples in the similar k-fold cross-validation but this time your parameters will be fixed. You can choose to run k-fold cross-validation once and get k error measures for each of the subset; 2*k if you consider training set which you could also look into.