I’ve been using both Random Forest and XGBoost for classification tasks. In most cases, I notice that XGBoost gives slightly better accuracy. However, I’m unsure about the specific scenarios where one model might be preferred over the other. • What are the main differences between Random Forest and XGBoost in terms of performance, training time, and interpretability? • Are there certain datasets or problems where Random Forest is a better choice than XGBoost? • Is XGBoost always the better option for tabular data, or are there trade-offs I should be aware of? I’d appreciate insights or examples based on real-world experience. Continue reading...
