超越算法超参数：论机器学习应用中的预处理超参数及相关陷阱
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
AI总结：
本文指出预处理选项选择也是一种常被忽视的超参数调优，系统回顾并实证说明了算法与预处理超参数的不同处理方式及其陷阱，以提升ML预测建模质量。
摘要：
基于监督机器学习（ML）充分生成和评估预测模型往往具有挑战性，尤其是对于应用研究领域中经验较少的用户而言。在模型生成过程涉及超参数调优（即对不同类型超参数进行数据驱动的优化，以提高所得模型的预测性能）的场景中，需要特别关注。关于调优的讨论通常集中在ML算法的超参数上（例如，基于树的算法中每个终端节点的最小观测数）。在这种背景下，人们常常忽视的是，在数据被提供给算法之前所应用的预处理步骤也存在超参数（例如，如何处理数据中的缺失特征值）。因此，尝试不同预处理选项以改善模型性能的用户可能没有意识到，这构成了一种超参数调优形式，尽管是非正式且不系统的，从而可能未能报告或说明这种优化。为了阐明这一问题，本文回顾并从实证上说明了生成和评估预测模型的不同程序，明确讨论了应用型ML用户通常处理算法超参数和预处理超参数的不同方式。通过强调潜在陷阱，尤其是那些可能导致夸大性能声明的陷阱，本综述旨在进一步提高ML应用中预测建模的质量。
英文摘要：
Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research areas. Special attention is required in settings where the model generation process involves hyperparameter tuning, i.e. data-driven optimization of different types of hyperparameters to improve the predictive performance of the resulting model. Discussions about tuning typically focus on the hyperparameters of the ML algorithm (e.g., the minimum number of observations in each terminal node for a tree-based algorithm). In this context, it is often neglected that hyperparameters also exist for the preprocessing steps that are applied to the data before it is provided to the algorithm (e.g., how to handle missing feature values in the data). As a consequence, users experimenting with different preprocessing options to improve model performance may be unaware that this constitutes a form of hyperparameter tuning, albeit informal and unsystematic, and thus may fail to report or account for this optimization. To illuminate this issue, this paper reviews and empirically illustrates different procedures for generating and evaluating prediction models, explicitly addressing the different ways algorithm and preprocessing hyperparameters are typically handled by applied ML users. By highlighting potential pitfalls, especially those that may lead to exaggerated performance claims, this review aims to further improve the quality of predictive modeling in ML applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
