nds_selection

Select best subset of points by non dominated sorting with
hypervolume contribution for tie breaking. Works on an arbitrary dimension
of size two or higher. Returns a vector of indices of selected points.

Implements hyperband method for hyperparameter
tuning. Various termination criteria can be set and combined. The
class 'AutoTuner' provides a convenient way to perform nested
resampling in combination with 'mlr3'. The hyperband algorithm was
proposed by Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin
Rostamizadeh and Ameet Talwalkar (2018) <arXiv:1603.06560>.

Marc Becker

mlr3hyperband

Hyperband for 'mlr3'

Sebastian Gruber

Jakob Richter

Julia Moosbauer

Bernd Bischl

nds_selection function

<dl class="dl-horizontal">
<dt><code>points</code></dt><dd><code>matrix()</code>
Numeric matrix with each column corresponding to a point.</dd>
<dt><code>n_select</code></dt><dd><code>integer(1)</code>
Amount of points to select.</dd>
<dt><code>ref_point</code></dt><dd><code>integer()</code>
Reference point for hypervolume.</dd>
<dt><code>minimize</code></dt><dd><code>logical()</code>
Should the ranking be based on minimization? (Single bool for all dimensions,
or vector of bools with each entry corresponding to each dimension).</dd>
</dl>

Parameters

Best points w.r.t. non dominated sorting with hypervolume contrib. — nds_selection

Best points w.r.t. non dominated sorting with hypervolume contrib.

nds_selection: Best points w.r.t. non dominated sorting with hypervolume contrib.

Description

Arguments

Value

Parameters