Abstract
Previous chapters demonstrated the ways of visual discovery of patterns using different General Line Coordinates. This chapter demonstrates the hybrid visual and analytical way to enhance the estimation of accuracy and errors of machine leaning discovery. It focuses on improvement of k-fold cross validation. It provides: (1) a justification for the worst case estimates using the Shannon Function, (2) hybrid visual and analytical ways to get these estimates, and (3) illustrative case studies. The visual means include the point-to-point and GLC point-to-graph mappings of the n-D data to 2-D.
Science can progress on the basis of error as long as it is not trivial.
Albert Einstein
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Kovalerchuk, B. (2018). Enhancing Evaluation of Machine Learning Algorithms with Visual Means. In: Visual Knowledge Discovery and Machine Learning. Intelligent Systems Reference Library, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-73040-0_10
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DOI: https://doi.org/10.1007/978-3-319-73040-0_10
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