Hyperparameter Tuning

Hyperparameter Optimization involves systematically searching for the best combination of hyperparameters—settings outside the model’s learning process—that maximize performance metrics like accuracy or error reduction. Techniques include grid search, random search, Bayesian optimization, and evolutionary algorithms. Proper tuning enhances model accuracy, generalization, and efficiency, making it a critical step to develop robust machine learning models tailored to specific tasks.