Computational and Inferential Tools for Machine Learning Methods in Biostatistical Research

生物统计研究中机器学习方法的计算和推理工具

• 批准号: RGPIN-2017-06586
• 负责人: kustra, rafal
• 金额: $ 1.02万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635943
• 关键词: Computational; Inferential; Tools; Machine; Learning

Modern machine learning methods, such as boosting, support vector machines, or neural networks, have made great impact on statistical research and application mostly in terms of improved predictive and prognostic accuracy. Their enhanced abilities to model complex interactions and non-linear effects could also be utilized to explain the underlying physical or physiological phenomena and to generate specific scientific hypothesis for further study. In non-strictly predictive applications, use of many modern methods, however, is hampered by their black-box nature and by the lack of inferential tools that would allow to obtain statistical confidence measures on inferred relationships. The simplest statistical inference which is universal in classical models pertains to statements on individual covariates. For example, is covariate "Gender" an important factor in a model of disease progression? In classical models this is answered by calculating statistical inference quantities (p-values, confidence intervals) on a parameter (or small set of parameters) that are connected with "Gender" in a model. In contrast, machine learning methods utilize a non-parametric approach where covariates influence on the outcome is not controlled by a small set of parameters. Hence the classical approach is not applicable and an importance of any particular covariate in the model of the outcome is not easily tested. While many model-specific or approximate measures have been proposed, in particular Variable Importance Metric in a Random Forest model, there is no universal, statistically coherent approach present in literature. We propose to develop, validate, apply and disseminate - in the form of freely available software packages - a set of tools for classical inference that will allow researchers to test the importance and influence of covariates of interest in the non-parametric machine learning models of the outcome.

现代机器学习方法，例如Boosting、支持向量机或神经网络，对统计研究和应用产生了巨大影响，主要体现在提高预测和预后准确性方面。它们增强的模拟复杂相互作用和非线性效应的能力也可以用来解释潜在的物理或生理现象，并生成具体的科学假设以供进一步研究。然而，在非严格预测应用中，许多现代方法的使用受到其黑盒性质和缺乏能够获得推断关系的统计置信度测量的推断工具的阻碍。经典模型中普遍存在的最简单的统计推断涉及对个体协变量的陈述。例如，协变量“性别”是疾病进展模型中的重要因素吗？在经典模型中，这是通过计算与模型中的“性别”相关的参数（或一小组参数）的统计推断量（p 值、置信区间）来回答的。相比之下，机器学习方法采用非参数方法，其中协变量对结果的影响不受一小部分参数的控制。因此，经典方法不适用，并且结果模型中任何特定协变量的重要性都不容易测试。虽然已经提出了许多特定于模型的或近似的度量，特别是随机森林模型中的变量重要性度量，但文献中没有通用的、统计上一致的方法。我们建议以免费软件包的形式开发、验证、应用和传播一套经典推理工具，使研究人员能够测试非参数机器学习模型中感兴趣的协变量的重要性和影响。结果。