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!

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    $\begingroup$ 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

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