target_weight

For <code>uwot::umap()</code> and <code>embed::step_umap()</code>, this is a weighting factor
between data topology and target topology. A value of 0.0 weights entirely
on data, a value of 1.0 weights entirely on target. The default of 0.5
balances the weighting equally between data and target.

Many models contain tuning parameters (i.e. parameters that
cannot be directly estimated from the data). These tools can be used
to define objects for creating, simulating, or validating values for
such parameters.

Hannah Frick

dials

Tools for Creating Tuning Parameter Values

Max Kuhn

Posit Software, PBC 

target_weight function

<dl><dt>range</dt>
<dd>A two-element vector holding the defaults for the smallest and
largest possible values, respectively. If a transformation is specified,
these values should be in the transformed units.</dd>
<dt>trans</dt>
<dd>A <code>trans</code> object from the <code>scales</code> package, such as
<code>scales::transform_log10()</code> or <code>scales::transform_reciprocal()</code>. If not provided,
the default is used which matches the units used in <code>range</code>. If no
transformation, <code>NULL</code>.</dd></dl>

Arguments

Amount of supervision parameter — target_weight

<dl>

<dt>range</dt>
<dd>A two-element vector holding the defaults for the smallest and
largest possible values, respectively. If a transformation is specified,
these values should be in the transformed units.</dd>


<dt>trans</dt>
<dd>A <code>trans</code> object from the <code>scales</code> package, such as
<code>scales::transform_log10()</code> or <code>scales::transform_reciprocal()</code>. If not provided,
the default is used which matches the units used in <code>range</code>. If no
transformation, <code>NULL</code>.</dd>

</dl>

target_weight: Amount of supervision parameter

Description

Usage

Arguments

Details

Examples