tdmRegressSummary

return value from a prior call to <code><a rd-options="" href="/link/tdmRegressLoop?package=TDMR&version=2.2" data-mini-rdoc="TDMR::tdmRegressLoop">tdmRegressLoop</a></code>, an object of class <code>TDMregressor</code>.

result

a list from which we need here the following entries<dl class="dl-horizontal">
 <dt><code>NRUN</code></dt><dd>number of runs (outer loop)</dd>
 <dt><code>method</code></dt><dd></dd>
 <dt><code>VERBOSE</code></dt><dd></dd>
 <dt><code>dset</code></dt><dd>[NULL] if !=NULL, attach it to result</dd>
</dl>

opts

[NULL] if not NULL, add this data frame to the return value (may cost a lot of memory!)

dset

<code>result$y</code> is "OOB RMAE" on training set for methods RF or MC.RF.
<code>result$y</code> is "RMAE" on test set (=validation set) for all other methods.
<code>result$y</code> is the quantity which the tuner seeks to minimize.

Tuned Data Mining in R ('TDMR') performs the complete tuning of a
data mining task (predictive analytics, that is classification and regression).
Preprocessing parameters and modeling parameters can be tuned simultaneously.
It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and
allows integration of additional tuners. Noise handling in the data mining optimization
process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.

Wolfgang Konen

TDMR

Tuned Data Mining in R

tdmRegressSummary function

return value from a prior call to <code><a rd-options='' href='tdmRegressLoop'>tdmRegressLoop</a></code>, an object of class <code>TDMregressor</code>.

a list from which we need here the following entries<dl class='dl-horizontal'>
 <dt><code>NRUN</code></dt><dd>number of runs (outer loop)</dd>
 <dt><code>method</code></dt><dd></dd>
 <dt><code>VERBOSE</code></dt><dd></dd>
 <dt><code>dset</code></dt><dd>[NULL] if !=NULL, attach it to result</dd>
</dl>

tdmRegressSummary: Print summary output for `result` from `tdmRegressLoop` and add `result$y`.

Description

Usage

Arguments

Value

See Also