create.regr.task

create.classif.task

<code>[character(1)]</code> ID to use for <code><a rd-options="" href="/link/Task?package=mosmafs&version=0.1.2" data-mini-rdoc="mosmafs::Task">Task</a></code>.

<code>[named list]</code> with columns entries <code>$X</code>, <code>$Y</code>, and <code>$orig.features</code>.

data

<code>[numeric(1)]</code> cutoff at which to binarize target.

cutoff

Both <code>create.regr.task</code> and <code>create.classif.task</code> take a numeric target column
<code>Y</code>, but <code>create.classif.task</code> binarizes it on <code>cutoff</code> to create a classification
task, while <code>create.regr.task</code> creates a regression task.

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

create.regr.task function

<dl><dt>id</dt>
<dd><code>[character(1)]</code> ID to use for <code>Task</code>.</dd>
<dt>data</dt>
<dd><code>[named list]</code> with columns entries <code>$X</code>, <code>$Y</code>, and <code>$orig.features</code>.</dd>
<dt>cutoff</dt>
<dd><code>[numeric(1)]</code> cutoff at which to binarize target.</dd></dl>

Arguments

Create mlr-Task from Data — create.regr.task

<dl>

<dt>id</dt>
<dd><code>[character(1)]</code> ID to use for <code>Task</code>.</dd>


<dt>data</dt>
<dd><code>[named list]</code> with columns entries <code>$X</code>, <code>$Y</code>, and <code>$orig.features</code>.</dd>


<dt>cutoff</dt>
<dd><code>[numeric(1)]</code> cutoff at which to binarize target.</dd>

</dl>

create.regr.task: Create mlr-Task from Data

Description

Usage

Value

Arguments

See Also