AutoML-GPT: Large Language Model for AutoML
This work addresses the challenge of making machine learning accessible to users without deep domain knowledge, though it appears incremental by integrating existing tools with large language models.
The paper tackles the problem of automating machine learning tasks by introducing AutoML-GPT, a framework that uses large language models to guide users through data preprocessing, feature engineering, and model selection via a conversational interface, resulting in significantly reduced time and effort for machine learning tasks.
With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users access to a wide range of data preprocessing techniques, feature engineering methods, and model selection algorithms. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. Throughout the process, AutoML-GPT employs advanced techniques for hyperparameter optimization and model selection, ensuring that the resulting model achieves optimal performance. The system effectively manages the complexity of the machine learning pipeline, guiding users towards the best choices without requiring deep domain knowledge. Through our experimental results on diverse datasets, we have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks. Its ability to leverage the vast knowledge encoded in large language models enables it to provide valuable insights, identify potential pitfalls, and suggest effective solutions to common challenges faced during model training.