Go to AutoML 2025 ABCD Track homepageExploring One Million Machine Learning Pipelines: A Benchmarking Study
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Reproducibility: zip
TL;DR: This paper explores ML pipelines by benchmarking combinations of feature preprocessing techniques and classification models. It explores good/bad pipeline combinations and provides meta-knowledge datasets to support meta-learning research.
Abstract: Machine learning solutions are largely affected by the values of the hyperparameter of their algorithms. This has motivated a large number of recent research projects on hyperparameter tuning, with the proposal of several, and highly diverse, tuning approaches. Rather than proposing a new approach or identifying the best hyperparameter tuning approach, this paper looks for good machine learning solutions by exploring machine learning pipelines. For such, it benchmarks pipelines focusing on the interaction between feature preprocessing techniques and classification models. The study evaluates the effectiveness of pipeline combinations, identifying high-performing and underperforming combinations. Additionally, it provides meta-knowledge datasets without any optimization selection bias to foster research contributions in meta-learning, accelerating the development of meta-models. The findings provide insights into the most effective preprocessing and modeling combination, guiding practitioners and researchers in their selection processes.
Submission Number: 8
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