Abstract
This chapter introduces several typical model evaluation methods that have been widely applied to various practical applications. Model evaluation is traditionally an integral part of the machine learning model development process. It uses statistical methods to help determine the best machine learning model for a given dataset, and help understand how well the machine learning model will perform in the future. All the evaluations are dependent on the training and test datasets that are collected prior to the model development process.
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References
Claudia Perlich, Foster Provost, and Jeffrey S. Simonoff. Tree induction vs. logistic regression: A learning-curve analysis. J. Mach. Learn. Res., 4(null):211–255, December 2003.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Huang, X., Jin, G., Ruan, W. (2023). Model Evaluation Methods. In: Machine Learning Safety. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-19-6814-3_2
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DOI: https://doi.org/10.1007/978-981-19-6814-3_2
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