Auto Sklearn

Auto-sklearn is an automated machine learning (AutoML) system designed to automate the process of building machine learning models, optimizing for predictive performance. Current research focuses on improving ensemble methods within Auto-sklearn, exploring alternatives to greedy ensemble selection, and investigating the use of techniques like CMA-ES for more efficient and less prone to overfitting ensemble creation. These advancements aim to enhance both the accuracy and efficiency of AutoML pipelines, impacting various fields by streamlining the development of predictive models and making machine learning more accessible to non-experts.