tdmModSortedRFimport: Sort the input variables decreasingly by their RF-importance.

In TDMR: Tuned Data Mining in R

Description

Build a Random Forest using importance=TRUE. Usually the RF is smaller (50 trees), to speed up computation. Use na.roughfix for missing value replacement. Decide which input variables to keep and return them in SRF$input.variables

Usage

tdmModSortedRFimport(d_train, response.variable, input.variables, opts)

Arguments

d_train	training set
response.variable	the target column from d_train to use for the RF-model
input.variables	the input columns from d_train to use for the RF-model
opts	options, here we use the elements [defaults in brackets]: SRF.kind: ="xperc": keep a certain importance percentage, starting from the most important variable ="ndrop": drop a certain number of least important variables ="nkeep": keep a certain number of most important variables ="none": do not call tdmModSortedRFimport at all (see tdmRegress.r and tdmClassify.r) SRF.ndrop: [0] how many variables to drop (if SRF.kind=="ndrop") SRF.XPerc: [0.95] if >=0, keep that importance percentage, starting with the most important variables (if SRF.kind=="xperc") SRF.calc: [TRUE] =TRUE: calculate importance & save on SRF.file, =F: load from SRF.file (SRF.file = Output/<filename>.SRF.<response.variable>.Rdata) SRF.ntree: [50] number of RF trees SRF.verbose: [2] SRF.maxS: [40] how many variables to show in plot SRF.minlsi: [1] a lower bound for the length of SRF$input.variables RF.sampsize: sampsize for RF, set prior to calling this func via tdmModAdjustSampsize(opts$SRF.samp,...) GD.DEVICE: if !="non", then make a bar plot on current graphic device CLS.CLASSWT: class weight vector to use in random forest training

Value

SRF, a list with the following elements:

input.variables	the vector of input variables which remain after importance processing. These are sorted by decreasing importance.
s_input	all input.variables sorted by decreasing (**NEW**) importance
s_imp1	the importance for s_input
s_dropped	vector with name of dropped variables
lsd	length of s_dropped
perc	the percentage of total importance which is in the dropped variables
opts	some defaults might have been added

Author(s)

Wolfgang Konen, Patrick Koch [e-mail]

TDMR documentation built on March 3, 2020, 1:06 a.m.