NSF SI2 SSE: Improving Scikit-learn usability and automation

2018-04-23T23:27:34Z (GMT) by Andreas Mueller
Single slight lightning-talk<div><br></div><div><div>Machine learning is a central component in many data-driven research areas, but it's adoption is limited by the often complex choice of data processing, model, and hyper-parameter settings.</div><div>The goal of this project is to create software tools that enable automatic machine learning, that is solving predictive analytics tasks without requiring the user to explicitly</div><div>specify the algorithm or model hyper-parameters used for prediction.</div><div>The software developed in this will enable a wider use of machine learning, by providing tools to apply machine learning without requiring knowledge of the details of the algorithms involved.</div><div><br></div><div>The project extends the existing scikit-learn project, a machine learning library for Python, which is widely used in academic research across disciplines.</div><div>The project will add features to this library to lower the amount of expert knowledge required to apply models to a new problem, and to facilitate the interaction with automated machine learning systems.</div><div>The project will also create a separate software package that includes models for automatic supervised learning, with a very simple interface, requiring minimal user interaction. In contrast to existing research projects, this project focuses on creating easy-to-use tools that can be used by researchers without extensive training in machine learning or computer science.</div></div>