👉 Correlation engineering is a technique used in data science and machine learning to enhance the performance of models by strategically manipulating the relationships between different variables or features in a dataset. It involves creating or modifying correlations between these variables to improve the model's ability to learn and make accurate predictions. This can be achieved through methods like feature scaling, normalization, or by introducing new features that capture the underlying correlations more effectively. By optimizing these inter-variable relationships, correlation engineering aims to reduce noise, highlight important patterns, and ultimately lead to more robust and interpretable models.
