👉 Labels engineering, also known as label design or annotation, is a critical process in machine learning and data science where the target variables or labels are crafted, refined, or structured to improve the performance of predictive models. This process involves defining how data should be categorized or labeled based on the specific problem at hand, often requiring domain expertise to ensure the labels accurately reflect the underlying patterns or relationships in the data. Effective labels engineering can significantly enhance model accuracy and generalization by providing clear, consistent, and meaningful targets for the learning algorithm. It may include tasks such as defining thresholds for class boundaries, creating hierarchical or multi-label labels, and ensuring the labels are representative of the real-world scenarios the model will encounter.
