classify

model

data

other argument passed on to methods

If the classification method can produce a matrix of posterior
probabilities (see <code><a rd-options="" href="/link/posterior?package=classifly&version=0.4.1" data-mini-rdoc="classifly::posterior">posterior</a></code>), then that will be used to
calculate the <code><a rd-options="" href="/link/advantage?package=classifly&version=0.4.1" data-mini-rdoc="classifly::advantage">advantage</a></code>. Otherwise, the classify method
will be used and the advantage calculated using a k-nearest neighbours
approach.

internal

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

classifly

Explore Classification Models in High Dimensions

classify function

If the classification method can produce a matrix of posterior
probabilities (see <code><a rd-options='' href='posterior'>posterior</a></code>), then that will be used to
calculate the <code><a rd-options='' href='advantage'>advantage</a></code>. Otherwise, the classify method
will be used and the advantage calculated using a k-nearest neighbours
approach.

classify: Extract classifications from a variety of methods.

Description

Usage

Arguments