Techniques for Automated Machine Learning
This is an incremental review paper that summarizes existing AutoML techniques to help data scientists and domain experts automate machine learning processes.
The paper reviews current developments in Automated Machine Learning (AutoML), categorizing techniques into automated feature engineering, model and hyperparameter learning, and deep learning, and presents state-of-the-art methods like Bayesian optimization and reinforcement learning.
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.