VariableImportance: Variable Importance Measures

In qeML: Quick and Easy Machine Learning Tools

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 qe* function name.
nReps	Number of holdout sets to generate.
opts	R list of optional arguments for none, some or all of th functions in qeFtnList.

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.

qeML documentation built on Nov. 9, 2023, 5:07 p.m.