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- W4386015265 abstract "Probabilistic metric spaces are a natural generalization of metric spaces in which the function that computes the distance outputs a distribution on the real numbers rather than a single number. Such a function is called a distribution function. In this paper, we construct a distance for linear regression models using one type of probabilistic metric space called F-space. F-spaces use fuzzy measures to evaluate a set of elements under certain conditions. By using F-spaces to build a metric on machine learning models, we permit to represent more complex interactions of the databases that generate these models." @default.
- W4386015265 created "2023-08-21" @default.
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- W4386015265 date "2023-01-01" @default.
- W4386015265 modified "2023-10-02" @default.
- W4386015265 title "Measuring the Distance Between Machine Learning Models Using F-Space" @default.
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- W4386015265 doi "https://doi.org/10.1007/978-3-031-39965-7_26" @default.
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