Optimizing Hyperparameters in Machine Learning Models: Techniques and Applications

Abstract

Hyperparameter optimization is critical to enhancing the performance of machine
learning models. Unlike model parameters, hyperparameters govern the learning process and
model configuration, requiring careful tuning for optimal performance. This paper reviews the
state-of-the-art techniques for hyperparameter optimization before 2013, including grid search,
random search, and Bayesian optimization. Through empirical analysis on benchmark datasets,
this study evaluates the efficacy of these techniques in improving model accuracy and
generalization. The results demonstrate the importance of structured hyperparameter tuning in
achieving robust and efficient machine learning models.

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