2021/08/30 by Mohamadjavad Bahmani, Radwa El Shawi, Bahmani, Mohamadjavad +5
Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2108.13066
Presented on The Fifth International Workshop on Automation in Machine Learning, A workshop to be held in conjunction with the KDD 2021 Conference
arxiv created 2021/08/30 · openalex publication_date 2021/08/30 · arxiv updated 2021/08/31 · openalex created_date 2021/09/13 · openalex updated_date 2026/07/28
Novel technologies in automated machine learning ease the complexity of algorithm selection and hyperparameter optimization. Hyperparameters are important for machine learning models as they significantly influence the performance of machine learning models. Many optimization techniques have achieved notable success in hyperparameter tuning and surpassed the performance of human experts. However, depending on such techniques as blackbox algorithms can leave machine learning practitioners without insight into the relative importance of different hyperparameters. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters to discover the trend and gain insights, with empirical results based on six classifiers and 200 datasets. Our results enable users to decide whether it is worth conducting a possibly time-consuming tuning strategy, to focus on the most important hyperparameters, and to choose adequate hyperparameter spaces for tuning. The results of our experiments show that gradient boosting and Adaboost outperform other classifiers across 200 problems. However, they need tuning to boost their performance. Overall, the results obtained from this study provide a quantitative basis to focus efforts toward guided automated hyperparameter optimization and contribute toward the development of better-automated machine learning frameworks.