Explore classification models in high dimensions
Description
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.