Shouldn't we use gradient Boosting everywhere? All we can see today on Kaggle is XGBoost, CatBoost, LightGBM, etc. If they are so popular, shouldn't we start every problem with them? How to decide among them, kernel methods, trees, neural nets &

Some say that these complex models are not interpretable and some also say that we should not directly start with boosting techniques or other complex models. So what should be the deciding factor ? How to decide which model to start with ? If complex models aren't interpretable then why are they even used ??

boosting catboost datascience gradient how to decide kaggle kernel lightgbm popular trees xgboost