👉 Category engineering is a process within machine learning where the boundaries between different categories in a dataset are intentionally modified to improve model performance. This involves manipulating or creating new categories to better align with the underlying data distribution, often to enhance a model's ability to generalize or to address issues like class imbalance. Techniques can include adding or removing categories, merging or splitting existing ones, or even transforming features to create more meaningful distinctions. The goal is to optimize the model's learning process by making the categories more informative and relevant to the task at hand, ultimately leading to more accurate and robust predictions.
