How do I choose between using Random Forest or XGBoost for a high-dimensional tabular dataset?

All answers to this question.

From my experience at a fintech firm, XGBoost usually outperforms Random Forest on high-dimensional data because it utilizes gradient boosting to correct errors sequentially. However, Random Forest is much harder to overfit and requires almost no hyperparameter tuning to get a decent baseline. If your priority is a quick, robust model that stakeholders can understand via feature importance plots, start with Random Forest. But if you have the computational budget and need to squeeze out every bit of AUC or precision, XGBoost with proper early stopping is the gold standard for tabular data.

   Answered 2025-05-15 by Kimberly Vance

Have you considered how the presence of categorical variables might influence your choice, especially regarding one-hot encoding versus native handling?

   Answered 2025-05-18 by Joshua Reed

• Joshua, that is a great point! For high-cardinality features, XGBoost often struggles unless you encode them properly, whereas some implementations of Random Forest handle them more gracefully. If the user is using CatBoost or specific XGBoost versions, the native handling might actually save more time than manual preprocessing.

     Commented 2025-05-20 by Brian Marshall

I usually prefer Random Forest for the initial baseline because it’s parallelizable and handles outliers naturally without much fuss during the EDA phase.

   Answered 2025-05-22 by Heather Lawson

• Totally agree, Heather. Using Random Forest as a baseline helps identify feature importance quickly before moving to more complex boosting algorithms for final deployment.

     Commented 2025-05-23 by Megan Foster