Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.
|Author||Hadley Wickham <firstname.lastname@example.org>|
|Date of publication||2014-04-23 19:51:22|
|Maintainer||Hadley Wickham <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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