I'm using SVM binary classification to predict movement of NASDAQ stock prices. My question is regarding cross-validation. I will divide the training data into V subsets. Training will be performed on (V-1) subsets and then prediction on the V-th subset is recorded. This will be done V times.

(1) Is the best measure of accuracy the average of all V outcomes? Or perhaps the minimum?

(2) Should subsets be equal length? Random?

(2a) Can subsets overlap?

(3) Do I need to ensure that each subset has at least some minimum percentage of each class outcome (1 or 0) for the results to be valid?


1 Answer 1


The cross-validation procedure does not turn on the choice of algorithm.

  1. Yes - calculate the prediction error of the fitted models when predicting the V'th part of the data. Combine the V estimates of prediction average using a simple average.

  2. Subsets should be randomly sampled (roughly equally sized). 2a. Subsets should not overlap.

  3. No. As long as the sampling is random you are OK.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.