• Rahul Roy Devarakonda

A crucial step in the creation of machine learning models, hyperparameter tweaking has a big influence on computing efficiency, model generalization, and prediction performance. Large-scale machine learning systems cannot benefit from the time-consuming and computationally costly nature of traditional manual and grid search-based approaches for hyperparameter tuning. In order to systematically improve model performance, this study proposes an automated hyperparameter tuning method that makes use of cutting-edge optimization approaches including Bayesian Optimization, Genetic Algorithms, and Reinforcement Learning. In order to dynamically improve hyperparameter selection based on real-time input and assessment metrics, the suggested technique integrates adaptive learning methodologies. The study compares the efficacy, scalability, and efficiency of several cutting-edge hyperparameter tuning frameworks, such as Optuna, Hyperopt, and AutoML, in a variety of machine learning models, including support vector machines, gradient boosting machines, and deep neural networks. Additionally, we provide a comparative analysis of the effects of hyperparameter tweaking on various datasets and machine learning tasks, demonstrating the gains made in model resilience, accuracy, and training time. Empirical findings show that automated hyperparameter tuning maximizes resource consumption by eliminating pointless calculations while also outperforming conventional methods in terms of accuracy. Along with possible solutions, the discussion covers issues related to automated tuning, such as algorithm-specific restrictions, computational complexity, and overfitting concerns. The report's conclusion offers several suggestions for future research, such as including meta-learning strategies, tuning based on reinforcement learning, and the use of quantum computing to hyperparameter optimization.

Bayesian optimization, genetic algorithms, reinforcement learning, model performance, autoML, neural networks, gradient boosting, hyperparameter search, computational efficiency, meta-learning, quantum computing, overfitting mitigation, scalable artificial intelligence, and automated machine learning

"An Automated Hyperparameter Tuning Approach for Optimizing Machine Learning Model Performance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.295-302, May-2019, Available :http://www.jetir.org/papers/JETIR1905Z33.pdf

"An Automated Hyperparameter Tuning Approach for Optimizing Machine Learning Model Performance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp295-302, May-2019, Available at : http://www.jetir.org/papers/JETIR1905Z33.pdf