Assessing the Generalizability of a Performance Predictive Model

• URL: http://arxiv.org/abs/2306.00040v1
• Date: Wed, 31 May 2023 12:50:44 GMT
• Title: Assessing the Generalizability of a Performance Predictive Model
• Authors: Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Koro\v{s}ec, Tome Eftimov
• Abstract summary: We propose a workflow to estimate the generalizability of a predictive model for algorithm performance. The results show that generalizability patterns in the landscape feature space are reflected in the performance space.
• Score: 0.6070952062639761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model. The predictive model uses the feature representation of a set of problem instances as input data and predicts the algorithm performance achieved on them. Common machine learning models struggle to make predictions for instances with feature representations not covered by the training data, resulting in poor generalization to unseen problems. In this study, we propose a workflow to estimate the generalizability of a predictive model for algorithm performance, trained on one benchmark suite to another. The workflow has been tested by training predictive models across benchmark suites and the results show that generalizability patterns in the landscape feature space are reflected in the performance space.

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