0
$\begingroup$

Are Grid Search CV & Randomized Search CV always/necessarily supposed to give more accurate results after hyperparameter tuning as compared to K-Fold Cross Validation?

$\endgroup$
2
  • 2
    $\begingroup$ Grid Search CV & Randomized Search CV are hyperparameter tuning strategies that use K-Fold Cross Validation internally to evaluate each hyperparameter configuration. Is this the intended question: does hyperparameter tuning consistently provide better model accuracy than not having hyperparameter tuning? $\endgroup$
    – grov
    Oct 7, 2021 at 4:07
  • $\begingroup$ Yes, this is what I meant. I first tried K Fold CV on my ML model, then Grid Search. My accuracy turned out to be slightly higher for K Fold though Grid & Radom Searches are expected to do better. So my doubt is, is it possible for K Fold to perform better than Grid Search? $\endgroup$
    – Apoorva
    Oct 7, 2021 at 5:34

1 Answer 1

0
$\begingroup$

From your comment above, " though Grid & Radom Searches are expected to do better." They are EXPECTED to perform better but it is not a given that in each and every case they will outperform K Fold CV. Sometimes K FoldCV can outperform Grid or Random SearchCV.

$\endgroup$

Your Answer

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

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