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" 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" 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