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cawley10a.pdf

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📊 Size: 741 KB

📄 Pages: 29 pages

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Summary

Model selection strategies for machine learning algorithms can lead to over-ﬁtting, resulting in poor generalisation performance, and subsequent selection bias in performance evaluation. The error of model selection criteria can be broken down into bias and variance components, with low variance being crucial to avoid over-ﬁtting. Common performance evaluation practices are susceptible to selection bias due to over-ﬁtting, making them unreliable. Methods to avoid over-ﬁtting in model selection and subsequent selection bias include treating model selection as an integral part of the model ﬁtting process and using robust unbiased performance evaluation protocols such as nested cross-validation. The differences in performance due to model selection are often comparable to the differences in performance between learning algorithms, making it essential to compare learning algorithm/model selection procedure combinations.

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Model selection strategies for machine learning algorithms can lead to over-ﬁtting, resulting in poor generalisation performance, and subsequent selection bias...

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File Format: PDF
File Size: 741 KB
Pages: 29
Language: EN
Total Downloads: 61
Last Updated: 3 weeks ago

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