👉 Parameter engineering is a critical process in machine learning and artificial intelligence that involves designing, optimizing, and selecting the parameters of a model to improve its performance. These parameters can include learning rates, regularization coefficients, network architecture components (like the number of layers or nodes), and other hyperparameters that govern how the model learns from data. The goal is to find the optimal set of parameters that maximizes the model's ability to generalize from training data to unseen data, thereby enhancing its predictive accuracy and robustness. This process often involves techniques such as grid search, random search, or more sophisticated methods like Bayesian optimization to systematically explore the parameter space and identify the best configuration.
