getnodes

Get terminal node observation assignments

Contrast trees represent a new approach for assessing the
accuracy of many types of machine learning estimates that are not
amenable to standard (cross) validation methods; see "Contrast
trees and distribution boosting", Jerome H. Friedman (2020)
<doi:10.1073/pnas.1921562117>. In situations where inaccuracies
are detected, boosted contrast trees can often improve
performance. Functions are provided to to build such trees in
addition to a special case, distribution boosting, an assumption
free method for estimating the full probability distribution of an
outcome variable given any set of joint input predictor variable
values.

Balasubramanian Narasimhan

conTree

Contrast Trees and Boosting

Jerome Friedman

getnodes function

<dl><dt>tree</dt>
<dd>model object output from contrast() or prune()</dd>
<dt>x</dt>
<dd>training input predictor data matrix or data frame in same format as in contrast()</dd></dl>

Arguments

Get terminal node observation assignments — getnodes

<dl>

<dt>tree</dt>
<dd>model object output from contrast() or prune()</dd>


<dt>x</dt>
<dd>training input predictor data matrix or data frame in same format as in contrast()</dd>

</dl>

getnodes: Get terminal node observation assignments

Description

Usage

Value

Arguments

See Also