tdmPreGroupLevels

dset

colname

list, here we need<ul>
<li>PRE.Xpgroup [0.99]</li>
<li>PRE.MaxLevel [32] (32 is the maximum number of levels allowed for <code><a rd-options="randomForest" href="/link/randomForest?package=TDMR&version=2.2&to=randomForest" data-mini-rdoc="randomForest::randomForest">randomForest</a></code>)</li>
</ul>

opts

This function reduces the number of levels for factor variables with too many levels.
It counts the cases in each level and orders them decreasingly. It binds the least
frequent levels together in a new level "OTHER" such that the remaining untouched
levels have more than opts$PRE.Xpgroup percent of all cases. OR it binds the levels with 
least cases together in "OTHER" such that the total number of new levels
is opts$PRE.MaxLevel. From these two choices for "OTHER" take the one which binds more 
variables in column "OTHER".

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

tdmPreGroupLevels function

list, here we need<ul>
<li>PRE.Xpgroup [0.99]</li>
<li>PRE.MaxLevel [32] (32 is the maximum number of levels allowed for <code><a rd-options='randomForest' href='randomForest'>randomForest</a></code>)</li>
</ul>

tdmPreGroupLevels: Group the levels of factor variable in `dset[,colname]`.

Description

Usage

Arguments

Value