prune

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 function

<dl><dt>tree</dt>
<dd>a tree model object output from contrast</dd>
<dt>thr</dt>
<dd>a split improvement threshold, default is 0.1</dd></dl>

Arguments

Prune a contrast tree — prune

<dl>

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


<dt>thr</dt>
<dd>a split improvement threshold, default is 0.1</dd>

</dl>

prune: Prune a contrast tree

Description

Usage

Value

Arguments