predtrast

Predict y-values from boosted contrast model

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

predtrast function

<dl><dt>model</dt>
<dd>model object output from modtrast()</dd>
<dt>x</dt>
<dd>x-values for new data</dd>
<dt>z</dt>
<dd>z-values for new data</dd>
<dt>num</dt>
<dd>number of trees used to compute model values</dd></dl>

Arguments

Predict y-values from boosted contrast model — predtrast

<dl>

<dt>model</dt>
<dd>model object output from modtrast()</dd>


<dt>x</dt>
<dd>x-values for new data</dd>


<dt>z</dt>
<dd>z-values for new data</dd>


<dt>num</dt>
<dd>number of trees used to compute model values</dd>

</dl>

predtrast: Predict y-values from boosted contrast model

Description

Usage

Value

Arguments

See Also