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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675391
• 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，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，主要是在提高预测和预后准确性方面。它们模拟复杂相互作用和非线性效应的增强能力也可用于解释潜在的物理或生理现象，并为进一步研究产生特定的科学假设。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推理工具的阻碍，这些工具可以获得推断关系的统计置信度。最简单的统计推断，这是普遍的，在经典的模型属于声明的个别协变量。例如，协变量“性别”是否是疾病进展模型中的重要因素？在经典模型中，这是通过计算模型中与“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，机器学习方法利用非参数方法，其中协变量对结果的影响不受一小组参数的控制。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不容易检验。虽然已经提出了许多模型特定的或近似的措施，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。我们建议开发，验证，应用和传播-以免费软件包的形式-一套经典推理工具，使研究人员能够测试结果的非参数机器学习模型中感兴趣的协变量的重要性和影响。