Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications

2024-12-04

arXiv.org

AI Summary

This paper identifies a systemic issue in machine learning: preprocessing hyperparameters—such as missing-value imputation strategies—are frequently overlooked yet substantially bias model evaluation. Current practice often involves informal, post-hoc tuning of preprocessing steps, leading to optimistic performance estimates and irreproducible results. To address this, the authors formally distinguish and empirically analyze the coupling effects between algorithmic and preprocessing hyperparameters. Using a modular supervised learning workflow model, controlled variable experiments, replication of canonical case studies, and bias diagnostics, they quantify the resulting optimistic bias. Key contributions include: (1) establishing preprocessing hyperparameters as equally critical as algorithmic ones; (2) proposing formal modeling principles to eliminate informal preprocessing tuning; and (3) delivering actionable reporting guidelines for ML practitioners, thereby significantly enhancing model credibility and reproducibility.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Addresses overlooked preprocessing hyperparameters in ML model tuning
• Highlights pitfalls in informal preprocessing optimization practices
• Aims to improve predictive modeling quality and reporting

Innovation

• Focuses on preprocessing hyperparameters tuning
• Highlights pitfalls in performance evaluation
• Improves predictive modeling quality