# Chossing between gradient boosting algorithms

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?

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