classifly: Explore classification models in high dimensions
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.