👉 Regression engineering is a set of practices and methodologies focused on optimizing the performance, accuracy, and efficiency of regression models in machine learning. It involves a systematic approach to model selection, feature engineering, hyperparameter tuning, and validation to ensure that the predictive models are robust and generalize well to unseen data. This process includes techniques like cross-validation to prevent overfitting, regularization methods to control model complexity, and the use of feature importance analysis to identify and select the most relevant predictors. By iteratively refining the model based on performance metrics and domain knowledge, regression engineering aims to create models that provide reliable and actionable insights while maintaining computational efficiency.
