naiveHoldoutDomHV

<code>[matrix]</code> fitness matrix of training data.

fitness

<code>[matrix]</code> fitness matrix of holdout data.

holdout

<code>[numeric]</code> reference point.

refpoint

Calculate dominated hypervolume on holdout data. The result is
biased depending on noise in holdout data performance.

Performs simultaneous hyperparameter tuning and
feature selection through both single-objective and multi-objective
optimization as described in Binder, Moosbauer et al. (2019) <arXiv:1912.12912>.
Uses the 'ecr'-package as basis but adds mixed integer
evolutionary strategies and multi-fidelity functionality as well as operators
specific for the problem of feature selection.

Martin Binder

mosmafs

Multi-Objective Simultaneous Model and Feature Selection

Susanne Dandl

Julia Moosbauer

naiveHoldoutDomHV function

<dl><dt>fitness</dt>
<dd><code>[matrix]</code> fitness matrix of training data.</dd>
<dt>holdout</dt>
<dd><code>[matrix]</code> fitness matrix of holdout data.</dd>
<dt>refpoint</dt>
<dd><code>[numeric]</code> reference point.</dd></dl>

Arguments

Naive Hypervolume on Holdout Data — naiveHoldoutDomHV

<dl>

<dt>fitness</dt>
<dd><code>[matrix]</code> fitness matrix of training data.</dd>


<dt>holdout</dt>
<dd><code>[matrix]</code> fitness matrix of holdout data.</dd>


<dt>refpoint</dt>
<dd><code>[numeric]</code> reference point.</dd>

</dl>

naiveHoldoutDomHV: Naive Hypervolume on Holdout Data

Description

Usage

Value

Arguments