SI2-SSE: Improving Scikit-Learn Usability and Automation

机器学习是许多数据驱动研究领域的核心组成部分，但它的采用往往受到数据处理、模型和参数设置的复杂选择的限制。这个项目的目标是创建能够实现自动机器学习的软件工具，也就是说，在不需要用户明确指定用于预测的算法或模型参数的情况下解决预测分析任务。在这个项目中开发的软件将通过提供工具来应用机器学习，而不需要了解所涉及的算法细节，从而使机器学习得到更广泛的应用。该项目由高级网络基础设施办公室和计算与通信基础部支持，扩展了现有的scikit-learn项目，这是一个Python机器学习库，广泛用于跨学科的学术研究。该项目将为该库添加功能，以降低将模型应用于新问题所需的专家知识数量，并促进与自动机器学习系统的交互。该项目还将创建一个单独的软件包，其中包括用于自动监督学习的模型，具有非常简单的界面，需要最少的用户交互。与现有的研究项目相比，该项目侧重于创建易于使用的工具，这些工具可以供没有经过机器学习或计算机科学广泛培训的研究人员使用。

Machine learning is a central component in many data-driven research areas, but its adoption is limited by the often complex choice of data processing, model, and parameter settings. 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 specify the algorithm or model parameters used for prediction. The software developed in this project 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.The project, supported by the Office of Advanced Cyberinfrastructure, and the Division of Computing and Communication Foundations, extends the existing scikit-learn project, a machine learning library for Python, which is widely used in academic research across disciplines. 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. 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.