review · Journal of Engineering Research and Reports
Hyperparameter tuning plays an essential role in maximising the performance and generalisation capabilities of machine learning models. Achieving strong outcomes depends on multiple interconnected factors, including data quality, algorithm choice, and overall model complexity. Specific hyperparameters, notably the learning rate and batch size, have a direct influence on how models train. A range of optimisation techniques can be deployed to identify effective configurations, spanning traditional approaches like grid search and random search to advanced strategies such as Bayesian optimisation and meta-learning. In deep learning architectures, setting and adjusting the learning rate requires dedicated strategies to ensure effective training. However, adopting these methods requires managing trade-offs, particularly between the computational costs of exploration and the resulting performance improvements. Understanding these techniques and their inherent challenges helps improve both the effectiveness and computational efficiency of automated learning workflows.
Machine learning systems are frequently deployed to automate complex tasks and generate predictions. Finding the right configurations for these models is often computationally expensive and time-consuming. Understanding various hyperparameter tuning methods helps technical teams choose appropriate strategies to boost model accuracy while keeping computing resource consumption and operational costs under control.
The findings inform software engineers, data scientists, and technical organisations developing machine learning solutions across diverse sectors. Applying structured tuning approaches can improve model efficiency and reduce infrastructure expenses. Because this work constitutes a broad review of existing optimisation methods rather than a tested software product or proprietary algorithm, it represents early-stage informational guidance rather than a directly commercialised technology.
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Hyperparameter tuning is essential for optimizing the performance and generalization of machine learning (ML) models. This review explores the critical role of hyperparameter tuning in ML, detailing its importance, applications, and various optimization techniques. Key factors influencing ML performance, such as data quality, algorithm selection, and model complexity, are discussed, along with the impact of hyperparameters like learning rate and batch size on model training. Various tuning methods are examined, including grid search, random search, Bayesian optimization, and meta-learning. Special focus is given to the learning rate in deep learning, highlighting strategies for its optimization. Trade-offs in hyperparameter tuning, such as balancing computational cost and performance gain, are also addressed. Concluding with challenges and future directions, this review provides a comprehensive resource for improving the effectiveness and efficiency of ML models.
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DOI: 10.9734/jerr/2024/v26i61188
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