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XGBoost is a powerful machine learning model that is used to handle tabular data efficiently, accurately, and interpretably. Bojan Tunguz, a quadruple Kaggle grandmaster, states that XGBoost is all you need. Recent experiments have shown that LLMs can be applied effectively for classification on tabular data, but the capabilities are still time consuming. Transformers are better suited for unstructured data, sequential data, and tasks that involve complex patterns. In Kaggle competitions, LLMs, when provided with appropriate prompts, demonstrated predictive power, though not at the level of top-performing traditional models like XGBoost.
