posterior

model

data

<p>Every classification method seems to provide a slighly different
way of retrieving the posterior probability of group membership.  This
function provides a common interface to all of them</p>

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.

Hadley Wickham

posterior: Extract posterior group probabilities

Description

Usage

Arguments