AutoML Method

AutoML aims to automate the often tedious and complex process of building machine learning models, improving efficiency and accessibility. Current research emphasizes integrating large language models (LLMs) to control and guide the entire model development pipeline, including data preprocessing and hyperparameter optimization, often using a human-centered approach to improve usability. This focus on end-to-end automation and user interaction is driven by the need to make machine learning more accessible to non-experts and to improve the efficiency of model development for complex tasks, such as those involving multimodal data. Benchmarking efforts are also crucial for comparing different AutoML approaches and identifying areas for improvement.