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I just stepped in machine learning competitions and it looks like most of the mid-sized dataset competitions are won by Gradient boosting based models. However I came accross case where LightGBM,Catboost or Adaboost had very different scores.

Is there a method to choose between those algorithms?

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I would say Catboost and lightgbm perform similarly and its purely a matter of choice. Some of my colleagues prefered Catboost when dataset has lots of categorical columns, but I rarely saw any advantage over lightgbm.

there is a great article comparing CatBoost vs. Light GBM vs. XGBoost https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db

And another article comparing even more boosting algos https://medium.com/@divyagera2402/boosting-algorithms-adaboost-gradient-boosting-xgb-light-gbm-and-catboost-e7d2dbc4e4ca

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