3月 2026Plank的市場佔有率分析

Gradient Boosted Machines have gained a lot of traction in recent years due to their accuracy and their flexibility (in terms of hyperparameters and loss function choices). They have also gained popularity in online competitions because their performance on tabular data is really good, they require fewer data preprocessing and they can handle missing data. There are so many articles on the web about XGBoost and a few more about LightGBM and CatBoost. There are even articles that compare them in pairs. But few posts compare the three together in a comprehensive way. This article pretends to do exactly that. In this article, we will go over a quick introduction to Gradient-boosting Machines and the main pros and cons of the models mentioned. We also will be pointing out the fundamental differences between XGBoost, CatBoost, and LightGBM and giving a summary chart to make it easy for our readers to decide which model adapts better to their project.