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  "Title": "Variable Importance Plots",
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  "Description": "A general framework for constructing variable importance\nplots from various types of machine learning models in R. Aside\nfrom some standard model- specific variable importance\nmeasures, this package also provides model- agnostic approaches\nthat can be applied to any supervised learning algorithm. These\ninclude 1) an efficient permutation-based variable importance\nmeasure, 2) variable importance based on Shapley values\n(Strumbelj and Kononenko, 2014)\n<doi:10.1007/s10115-013-0679-x>, and 3) the variance-based\napproach described in Greenwell et al. (2018)\n<doi:10.48550/arXiv.1805.04755>. A variance-based method for\nquantifying the relative strength of interaction effects is\nalso included (see the previous reference for details).",
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        "vip.default",
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        "Model-specific importance",
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        "Plotting options",
        "References"
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