An Empirical Study on the Impact of Hyperparameter Tuning on Model Performance

Abstract

Hyperparameter tuning plays a critical role in optimizing the performance of both traditional machine learning (ML) and deep learning (DL) models. While model architecture and data quality significantly affect outcomes, improper selection of hyperparameters can lead to underfitting, overfitting, and inefficient training. This empirical study investigates the impact of hyperparameter tuning using multiple algorithms, including Support Vector Machines (SVM), Random Forest, Logistic Regression, and Deep Neural Networks (DNNs), across benchmark datasets. The study compares tuning techniques such as Grid Search, Random Search, Bayesian Optimization, and manual tuning. Results show that tuning leads to performance gains of up to 25% in accuracy and 30% in F1-score depending on the model and dataset. Conceptual figures are incorporated to illustrate performance trends and convergence improvements. The findings highlight that hyperparameter tuning is not equally beneficial across models; deep learning models show the highest sensitivity to tuning, whereas simple models gain marginal improvements. The study concludes that a systematic and computationally efficient tuning strategy is essential for maximizing model performance, particularly in resource-constrained environments.

Introduction

Hyperparameter tuning is critical for optimizing the performance of machine learning (ML) and deep learning (DL) models. Unlike trainable parameters, hyperparameters—such as learning rates, regularization strength, layer sizes, and batch sizes—are set before training and strongly influence how models learn from data. While traditional ML models like Logistic Regression, SVM, KNN, and Random Forest have relatively small hyperparameter spaces and are moderately sensitive to tuning, deep learning models are highly sensitive due to their large, complex parameter spaces.

Common tuning strategies include manual tuning, Grid Search, Random Search, and Bayesian Optimization. Manual tuning offers modest improvements but is time-consuming and subjective. Grid Search is effective for traditional models but computationally expensive for deep networks. Random Search provides efficient stochastic exploration and often matches or exceeds Grid Search performance in high-dimensional spaces. Bayesian Optimization achieves the best results overall, improving accuracy while reducing computational cost, especially for deep learning models.

Empirical findings show that hyperparameter tuning yields the largest performance gains for complex models: SVM, Random Forest, and deep neural networks improve by 15–25%, while simpler models benefit less. Tuning also affects convergence behavior, computational cost, and model robustness. Sensitivity analysis confirms that complex models are highly dependent on proper hyperparameter selection. Statistical tests demonstrate that performance improvements from systematic tuning are significant.

The study emphasizes that hyperparameter tuning should be an integral part of ML workflows, with Bayesian Optimization preferred for deep learning and Random Search as an efficient alternative when resources are limited.

Conclusion

This empirical study demonstrates that hyperparameter tuning has a profound impact on the performance of both traditional machine learning models and deep learning models, albeit to varying degrees. Through the evaluation of multiple models—including Logistic Regression, SVM, Random Forest, KNN, Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs)—across diverse datasets and tuning strategies, the study confirms that hyperparameter tuning is a critical component of model optimization rather than a secondary or optional step in the machine learning pipeline. The comparative results reveal that while default hyperparameters often yield moderate baseline performance, they fail to exploit the full potential of the models. Traditional machine learning models demonstrated modest improvements with tuning, particularly SVM and Random Forest, which are highly sensitive to kernel choices and tree-based hyperparameters. Logistic Regression, being relatively robust by design, showed minimal changes, reinforcing the notion that simpler models are less dependent on hyperparameter optimization. KNN benefited from tuning of the number of neighbors and distance metrics, but remained limited compared to more flexible algorithms. In contrast, deep learning models showed dramatic performance gains when tuned appropriately. Default configurations frequently led to poor convergence or suboptimal accuracy, confirming earlier studies that emphasize the sensitivity of deep architectures to learning rate, layer size, optimizer, and regularization (Goodfellow, Bengio, and Courville 104). With systematic tuning, DNN accuracy increased by over 20% in some cases, while CNN performance improved to near state-of-the-art levels. These results underscore the fact that deep learning models inherently possess high representational capacity, but can only achieve peak performance through careful hyperparameter configuration. The evaluation of tuning strategies revealed meaningful trade-offs between performance and computational cost. Grid Search produced strong results for traditional models with limited hyperparameters, but became inefficient when applied to deep learning due to the exponential growth of parameter combinations. Random Search proved more resource-efficient and often discovered better configurations by exploring a broader range of values, aligning with prior research advocating its effectiveness in high-dimensional search spaces (Bergstra and Bengio 289). Bayesian Optimization emerged as the most effective strategy overall, achieving the highest accuracies with fewer iterations and intelligently guiding the search process based on robabilistic modeling (Snoek, Larochelle, and Adams 2964). However, Bayesian methods required more complex implementation and were computationally heavier than Random Search, suggesting they are most suitable in environments with moderate to high resources. The study also found that hyperparameter tuning must align with broader project goals, such as interpretability, computational budget, or fairness.

References

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Copyright

Copyright © 2025 Dr. V. Yuvaraj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.