Local Individual Conditional Expectation is as an extension to Individual Conditional Expectation (ICE) and provides three-dimensional local explanations for particular data instances. The three dimension are two features at the horizontal and vertical axes as well as the target that is represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. The plot for discrete targets looks similar to plots of cluster algorithms like k-means, where different clusters represent different predictions. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.
|Author||Martin Walter [aut, cre]|
|Maintainer||Martin Walter <[email protected]>|
|License||BSD_3_clause + file LICENSE|
|Package repository||View on CRAN|
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