Vol. 8, Special Issue 3 (2019)

In the burgeoning field of machine learning, the success of a model hinges on the judicious selection of hyperparameters-parameters external to the model itself that govern its learning process. This paper systematically examines the underlying principles of hyperparameter tuning, elucidating its significance in fine-tuning model behavior to meet the demands of diverse datasets and tasks.

The review commences with a comprehensive overview of the major hyperparameters influencing model performance, such as learning rates, regularization terms, and architectural parameters. Subsequently, it delves into the intricate relationships between these hyperparameters, dissecting their impact on model convergence, generalization, and robustness. Theoretical frameworks are presented to unravel the mathematical underpinnings of hyperparameter tuning, providing a deeper understanding of the optimization landscape.

The paper also explores state-of-the-art hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization. A critical analysis of the advantages and limitations of each method is presented, offering insights into the trade-offs involved in selecting an appropriate optimization strategy.

Furthermore, the review scrutinizes the transferability of hyperparameter configurations across different datasets and domains. It investigates the challenges posed by non-convex optimization landscapes and the potential pitfalls associated with over fittinghy per parameters to specific datasets.