nsel

an object of <code><a rd-options="" href="/link/mhingebst?package=bst&version=0.3-23" data-mini-rdoc="bst::mhingebst">mhingebst</a></code>, <code><a rd-options="" href="/link/mbst?package=bst&version=0.3-23" data-mini-rdoc="bst::mbst">mbst</a></code>, or <code><a rd-options="" href="/link/rmbst?package=bst&version=0.3-23" data-mini-rdoc="bst::rmbst">rmbst</a></code>

object

mstop

Find Number of Variables In Multi-class Boosting Iterations

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Zhu Wang

Gradient Boosting

Torsten Hothorn

nsel function

<dl> <dt>object</dt>
<dd>an object of <code>mhingebst</code>, <code>mbst</code>, or <code>rmbst</code></dd> <dt>mstop</dt>
<dd>boosting iteration number</dd></dl>

Arguments

Author

Find Number of Variables In Multi-class Boosting Iterations — nsel

<dl>

 <dt>object</dt>
<dd>an object of <code>mhingebst</code>, <code>mbst</code>, or <code>rmbst</code></dd>

 <dt>mstop</dt>
<dd>boosting iteration number</dd>

</dl>

nsel: Find Number of Variables In Multi-class Boosting Iterations

Description

Usage

Value

Arguments

Author