Feature engineering remains one of the most effective ways to improve model accuracy when working with tabular data. Unlike domains such as NLP and computer vision, where neural networks can extract rich patterns from raw inputs, the best-performing tabular models��particularly gradient-boosted decision trees��still gain a significant advantage from well-crafted features. However��
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