Automated Machine Learning: Tools and Techniques for Model Selection and Hyperparameter Tuning

Abstract

Automated Machine Learning (AutoML) has emerged as a powerful paradigm to streamline the process of building and deploying machine learning models by automating key tasks such as model selection and hyperparameter tuning. This abstract explores the tools and techniques available in the AutoML landscape, focusing on their capabilities, limitations, and potential impact on the field of machine learning. AutoML tools aim to democratize machine learning by enabling users with varying levels of expertise to leverage advanced models and algorithms without extensive manual intervention. These tools typically offer a range of functionalities, including automated data preprocessing, feature engineering, model selection, hyperparameter optimization, and model interpretation. However, despite its promise, AutoML is not without challenges. The performance of AutoML tools can be highly dependent on the quality and characteristics of the input data, and they may struggle with complex or domain-specific tasks that require specialized expertise. Moreover, the black-box nature of some AutoML algorithms can limit the interpretability and explainability of the resulting models, raising concerns about transparency and accountability.