0
$\begingroup$

I have noticed while working with multiple datasets that catboost with its default parameters tends to outperform lightgbm or xgboost with its default parameters even on a tabular dataset with no categorical features.

I am assuming this has something to do with the way catboost constructs the decision trees but I just wanted to confirm this theory. If anyone could elaborate on why it performs better on non categorical data then that would be great! Thanks in advance!

$\endgroup$
1
  • 1
    $\begingroup$ it is a theorem that no algorithm can consistently outperform all other algorithms under all circumstances (no free lunch theorem), so most probably what you observe is simply coincidence $\endgroup$ – Nikos M. Feb 20 at 18:22

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.