Variable Importance Measures {qeML} | R Documentation |
Variable Importance Measures
Description
Various approaches to assessing relative importance of one's features.
Usage
qeLeaveOut1Var(data,yName,qeFtnName,nReps,opts=list())
Arguments
data |
Dataframe, training set. Classification case is signaled via labels column being an R factor. |
yName |
Name of the class labels column. |
qeFtnName |
Quoted |
nReps |
Number of holdout sets to generate. |
opts |
R list of optional arguments for none, some or all of th
functions in |
Details
Many methods have been developed assessing relative importance of one's features. A few that we consider most useful are accessible here.
As a quick assessment, the qeLeave1VarOut
function, with call
form as above, simply compares predictive ability with and without
the given feature.
Some methods rely on reweighting:
-
qeKNN
-
qeRFranger
Others make use of order of entry of a variable into the prediction model:
-
qeFOCI
-
qeLASSO
Author(s)
Norm Matloff
Examples
data(pef)
qeLeaveOut1Var(pef,'wageinc','qeLin',5)
# in order of impact, wkswrkd largest, then education etc.