8
$\begingroup$

By increasing the size of the training set the model memorize more data. Thus, will using leave one out increase the chance of overfitting?

$\endgroup$

1 Answer 1

1
$\begingroup$

ML model will start to "memorize" data with increasing complexity of your algorithm (and its parameters), not the size of training set.

Cross-validation is used to estimate your model performance on data that was not used to train. If you use LOOCV (k=n) then your k models will be (almost) identical. This gives you a high variance in your model evaluation and low bias regarding the final model trained on the entire data set.

Use 10-times 10-fold stratified CV if you unsure about a good value for k.

$\endgroup$
1
  • 2
    $\begingroup$ The part about high variance is not always true and is an incorrect generalization of a special case for unstable algorithms - see recent posts about bias variance trade-off around the site $\endgroup$ Commented Oct 26, 2018 at 14:47

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.