Veridical data science
Veridical data science
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DOI：
10.1073/pnas.1901326117
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发表时间：
2020-02-25
影响因子：
11.1
通讯作者：
Kumbier, Karl
中科院分区：
文献类型：
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作者：
Yu, Bin;Kumbier, Karl
在统计学、机器学习和科学探究的基础上，我们提出了可预测性、可计算性和稳定性（PCS）框架。我们的框架由工作流和文档组成，旨在在整个数据科学生命周期中提供负责任、可靠、可重复和透明的结果。PCS工作流程使用可预测性作为现实检查，并考虑计算在数据收集/存储和算法设计中的重要性。它通过一个总体稳定性原则增强了可预测性和可计算性。稳定性扩展了统计不确定性考虑因素，以评估人类判断调用如何通过数据和模型/算法扰动影响数据结果。作为PCS工作流程的一部分，我们开发PCS推理程序，即PCS扰动间隔和PCS假设检验，以调查相对于问题制定，数据清理，建模决策和解释的数据结果的稳定性。我们通过我们自己和其他人的神经科学和基因组学项目来说明PCS推理。此外，我们证明了其良好的性能，在接受者工作特征（ROC）曲线在高维，稀疏线性模型模拟，包括广泛的错误指定的模型。最后，我们提出了基于R Markdown或Markyter Notebook的PCS文档，其中包含公开可用的可复制代码和叙述，以支持整个分析过程中的人类选择。在Zenodo上提供的基因组学案例研究中演示了PCS工作流程和文档。
Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.