👉 Label engineering is the process of designing and optimizing the labels used in machine learning models to improve their performance and generalization capabilities. It involves carefully crafting the input features, target variables, and even the structure of the dataset to better align with the learning task at hand. This can include techniques such as feature selection and extraction, data augmentation, and the creation of composite labels or multi-label tags. By thoughtfully engineering labels, practitioners can reduce noise, highlight relevant patterns, and enhance the model's ability to generalize from the training data to unseen examples, ultimately leading to more accurate and robust machine learning models.
