Exploring Hyperparameter Tuning Strategies for Optimizing Model Performance
Anitha Julian, R. Devipriya · 2024
The proposed work explores different machine learning hyperparameter tuning techniques to maximize model performance. By systematically adjusting hyperparameters, such as learning rates, regularization strengths, and network architectures, models can better capture underlying patterns in data and improve predictive accuracy. The study looks on commonly utilized hyperparameter tuning techniques. The benefits, drawbacks, and suitability for various situations of each technique are examined. Furthermore, the efficiency and scalability of automated frameworks for hyperparameter tuning are assessed to maximize model performance. The information gathered from this study provides practitioners and researchers with helpful guidance for optimizing their machine learning models through hyperparameter adjustments.