How do I choose between Random Forest and XGBoost for high-dimensional financial datasets?

All answers to this question.

For financial datasets where interpretability is as crucial as performance, Random Forest is often the safer bet for initial baselines because it’s harder to overfit. However, if you are looking for peak predictive power and can spend time on hyperparameter tuning, XGBoost is superior, especially with sparse data. In credit scoring, you must explain 'why' a loan was denied. While XGBoost has SHAP values for feature importance, Random Forest’s bagging approach is inherently more stable across different folds of data. I’d suggest starting with a tuned XGBoost model and comparing the AUC-ROC metrics.

   Answered 2025-05-18 by Samantha Reed

Have you considered how the presence of multicollinearity among your 200 features might be affecting the gain calculations in your gradient boosting iterations?

   Answered 2025-05-20 by Kevin Walsh

• Multicollinearity can definitely skew feature importance. To address this, I usually run a Variance Inflation Factor (VIF) analysis first or use Principal Component Analysis to reduce dimensionality. In XGBoost specifically, high correlation between features can make the model pick one arbitrarily, which ruins the "reason codes" needed for financial compliance and reporting.

     Commented 2025-05-22 by Brian Foster

I suggest using LightGBM if your dataset is massive; it’s faster than XGBoost and handles categorical features natively without needing one-hot encoding, which saves memory.

   Answered 2025-05-25 by Laura Bennett

• I agree with Laura. LightGBM's leaf-wise growth often results in lower loss than the level-wise growth used in other frameworks, making it a trending choice for Kaggle-style problems.

     Commented 2025-05-26 by Michael Donovan