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  "Description": "Local Individual Conditional Expectation ('localICE') is a\nlocal explanation approach from the field of eXplainable\nArtificial Intelligence (XAI). localICE is a model-agnostic XAI\napproach which provides three-dimensional local explanations\nfor particular data instances. The approach is proposed in the\nmaster thesis of Martin Walter as an extension to ICE (see\nReference). The three dimensions are the two features at the\nhorizontal and vertical axes as well as the target represented\nby different colors. The approach is applicable for\nclassification and regression problems to explain interactions\nof two features towards the target. For classification models,\nthe number of classes can be more than two and each class is\nadded as a different color to the plot. The given instance is\nadded to the plot as two dotted lines according to the feature\nvalues. The localICE-package can explain features of type\nfactor and numeric of any machine learning model. Automatically\nsupported machine learning packages are 'mlr', 'randomForest',\n'caret' or all other with an S3 predict function. For further\nmodel types from other libraries, a predict function has to be\nprovided as an argument in order to get access to the model.\nReference to the ICE approach: Alex Goldstein, Adam Kapelner,\nJustin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.",
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  "Repository": "https://viadee.r-universe.dev",
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