可重复模型提炼的通用方法

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Pub Date : 2024-08-05 DOI:10.1007/s10994-024-06597-w
Yunzhe Zhou, Peiru Xu, Giles Hooker
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引用次数: 0

摘要

模型提炼一直是产生可解释机器学习的流行方法。它使用可解释的 "学生 "模型来模仿黑盒 "教师 "模型的预测。然而,当学生模型对用于训练的数据集的可变性很敏感时,即使教师模型保持不变,相应的解释也不可靠。现有的策略通过检查是否生成了足够大的伪数据样本来可靠地重现学生模型,从而稳定模型提炼,但迄今为止,针对每一类特定学生模型的方法都是单独开发的。在本文中,我们基于中心极限定理,针对学生对教师的估计保真度,开发了一种通用的稳定模型提炼方法。我们从候选学生模型集合开始,寻找与教师合理一致的候选模型。然后,我们构建了一个多重测试框架,以选择样本大小,从而在不同的伪样本下选出一致的学生模型。我们在决策树、下降规则列表和符号回归这三种常用智能模型上演示了我们提出的方法的应用。最后,我们在乳腺肿块和乳腺癌数据集上进行了模拟实验,并通过马尔可夫过程的理论分析说明了测试过程。代码可在 https://github.com/yunzhe-zhou/GenericDistillation 公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A generic approach for reproducible model distillation

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable “student” model to mimic the predictions made by the black box “teacher” model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough sample of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed separately for each specific class of student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the estimated fidelity of the student to the teacher. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a sample size such that the consistent student model would be selected under different pseudo samples. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process. The code is publicly available at https://github.com/yunzhe-zhou/GenericDistillation.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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