所有的模型都是错误的,但许多是有用的:通过同时研究整个预测模型类来了解变量的重要性。

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2019-01-01
Aaron Fisher, Cynthia Rudin, Francesca Dominici
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引用次数: 0

摘要

变量重要性(VI)工具描述了协变量对预测模型准确性的影响程度。然而,对于一个表现良好的模型(例如,具有固定系数向量β的线性模型f (x) = x T β)的重要变量对于另一个模型可能不重要。在本文中,我们提出模型类依赖(MCR)作为VI值在预先指定的类中所有表现良好的模型的范围。因此,MCR通过考虑到许多预测模型(可能具有不同的参数形式)可能很好地拟合数据这一事实,给出了更全面的重要性描述。在推导MCR的过程中,我们展示了基于随机森林中使用的VI度量的基于排列的VI估计的几个信息结果。具体来说,我们推导了单个预测模型的排列重要性估计、u统计量、条件变量重要性、条件因果效应和线性模型系数之间的联系。然后,我们使用一种新颖的、可推广的技术,给出了MCR的概率界限。我们将MCR应用于布劳沃德县犯罪记录的公共数据集,以研究累犯预测模型对性别和种族的依赖。在此应用程序中,MCR可用于帮助VI了解未知的专有模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

All Models are Wrong, but <i>Many</i> are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.

All Models are Wrong, but <i>Many</i> are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.

All Models are Wrong, but <i>Many</i> are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.

Variable importance (VI) tools describe how much covariates contribute to a prediction model's accuracy. However, important variables for one well-performing model (for example, a linear model f (x) = x T β with a fixed coefficient vector β) may be unimportant for another model. In this paper, we propose model class reliance (MCR) as the range of VI values across all well-performing model in a prespecified class. Thus, MCR gives a more comprehensive description of importance by accounting for the fact that many prediction models, possibly of different parametric forms, may fit the data well. In the process of deriving MCR, we show several informative results for permutation-based VI estimates, based on the VI measures used in Random Forests. Specifically, we derive connections between permutation importance estimates for a single prediction model, U-statistics, conditional variable importance, conditional causal effects, and linear model coefficients. We then give probabilistic bounds for MCR, using a novel, generalizable technique. We apply MCR to a public data set of Broward County criminal records to study the reliance of recidivism prediction models on sex and race. In this application, MCR can be used to help inform VI for unknown, proprietary models.

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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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