AutoML Techniques for Automated Model Selection and Hyperparameter Optimization

Abstract

Selecting optimal machine learning models and tuning hyperparameters is a time-consuming process. This research presents an AutoML framework that automates model selection using Bayesian optimization and neural architecture search. The system evaluates multiple models and configurations to identify the best-performing solution with minimal human intervention. Results indicate significant improvements in efficiency and performance, enabling faster deployment of ML solutions.