用条件谢林值解释机器学习预测，支持时间序列与因果分析。
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
- 基于条件谢林值，精准捕捉特征间依赖关系
- 支持时序预测解释，可并行计算且自动检测收敛
- 适合需要模型可解释性的数据科学家和工程师
本文介绍了shapr R包，一个用于生成机器学习与统计回归模型预测解释的多功能工具。shaprpy Python库将shapr的核心功能引入Python生态。谢林值源自20世纪50年代合作博弈论，近年来广泛用于量化特征对特定预测结果的贡献。shapr强调条件谢林值估计，提供多种方法以准确捕获特征依赖性——这是正确模型解释中常被忽略的关键点。除常规表格数据外，shapr R包还包含专门用于解释时间序列预测的功能。该包采用极简用户函数设计，多数场景下具备合理默认值，同时为高级用户提供充分灵活性以微调计算过程。附加功能包括并行计算、迭代估计与收敛检测，以及丰富的可视化工具。当具备因果信息时，shapr还可计算因果与非对称谢林值。总体而言，shapr与shaprpy旨在通过强大且易用的框架提升预测模型的可解释性。
原文摘要 · Abstract (English)
This paper introduces the shapr R package, a versatile tool for generating Shapley value-based prediction explanations for machine learning and statistical regression models. Moreover, the shaprpy Python library brings the core capabilities of shapr to the Python ecosystem. Shapley values originate from cooperative game theory in the 1950s, but have over the past few years become a widely used method for quantifying how a model's features/covariates contribute to specific prediction outcomes. The shapr package emphasizes conditional Shapley value estimates, providing a comprehensive range of approaches for accurately capturing feature dependencies -- a crucial aspect for correct model explanation, typically lacking in similar software. In addition to regular tabular data, the shapr R package includes specialized functionality for explaining time series forecasts. The package offers a minimal set of user functions with sensible default values for most use cases while providing extensive flexibility for advanced users to fine-tune computations. Additional features include parallelized computations, iterative estimation with convergence detection, and rich visualization tools. shapr also extends its functionality to compute causal and asymmetric Shapley values when causal information is available. Overall, the shapr and shaprpy packages aim to enhance the interpretability of predictive models within a powerful and user-friendly framework.
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