When an ML model has to deal with tabular data, it’s XGBoost (Extreme Gradient Boosting) that energises the model’s performance and computational speed. XGBoost stands as a tree-based ensemble machine learning algorithm renowned for its superior predictive capabilities and performance. The powerful machine learning algorithm is more capable of training a model to find patterns in a dataset with labels and features than LLMs.
XGBoost should be considered for any supervised learning task when there’s a substantial number of training examples. Besides, it excels when dealing with a blend of categorical and numeric features. It is particularly effective in scenarios where the dataset comprises a mix of these feature types or when the developer is exclusively working with numeric features.
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