prune.seq

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

prune.seq function

<dl><dt>tree</dt>
<dd>a tree model object output from contrast</dd>
<dt>eps</dt>
<dd>small increment defining grid of threshold values</dd>
<dt>plot.it</dt>
<dd>a logical flag indicating plot/don't plot of number of nodes versus threshold value for all pruned subtrees, default <code>TRUE</code></dd></dl>

Arguments

Show all possible pruned subtrees — prune.seq

<dl>

<dt>tree</dt>
<dd>a tree model object output from contrast</dd>


<dt>eps</dt>
<dd>small increment defining grid of threshold values</dd>


<dt>plot.it</dt>
<dd>a logical flag indicating plot/don't plot of number of nodes versus threshold value for all pruned subtrees, default <code>TRUE</code></dd>

</dl>

prune.seq: Show all possible pruned subtrees

Description

Usage

Value

Arguments