Optimizing Hyperparameters: Techniques for Improving Machine Learning Models

Abstract

Improving machine learning models' performance, effectiveness, and generalization requires careful hyperparameter adjustment.Model accuracy may be greatly increased, mistakes can be decreased, and adaptability to new data can be improved by choosing the appropriate hyperparameters.Grid Search, Random Search, Bayesian Optimization, gradient-based methods, and population-based approaches are among the well-known hyperparameter optimization strategies reviewed in this study.Every approach has advantages and disadvantages of its own, particularly when it comes to striking a balance between exploration and computing efficiency. We evaluate the effects of these methods on different machine learning and deep learning tasks in terms of model performance, training duration, and resource consumption.We also look at new developments that try to speed up the optimization process, such as Automated Machine Learning (AutoML) and Transfer Learning.This paper offers useful insights to assist practitioners in choosing the best optimization techniques based on various models and datasets through case studies and experimental findings.The results highlight how crucial hyperparameter tweaking is as a first step in creating reliable and effective machine learning systems.