How to use LOOCV to find a subset that classifies better than full set in R

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I am working with the wbca data from the faraway package. The prior probability of sampling a malignant tumor is π0 = 1/3 and the prior probability for sampling a benign tumor is π1 = 2/3.

I am trying to use the naive Bayes classifier with multinomials to see if there is a good subset of the 9 features that classifies better than the full set using LOOCV.

I am unsure where to even begin with this, so any Rcode help would be great. Thanks!

naivebayes probability statistics r
1 Answers

You can try something below, the kernel estimate of your predictors might not be the most accurate, but it's something you can start with:

library(faraway)
library(naivebayes)
library(caret)

x = wbca[,!grepl("Class",colnames(wbca))]
y = factor(wbca$Class)

ctrl <- rfeControl(functions = nbFuncs,
                   method = "LOOCV")

bayesProfile <- rfe(x, y,
                 sizes = subsets,
                 rfeControl = ctrl)

bayesProfile

Recursive feature selection

Outer resampling method: Leave-One-Out Cross-Validation 

Resampling performance over subset size:

 Variables Accuracy  Kappa Selected
         2   0.9501 0.8891         
         3   0.9648 0.9225         
         4   0.9648 0.9223         
         5   0.9677 0.9290         
         6   0.9750 0.9454        *
         7   0.9692 0.9322         
         8   0.9750 0.9455         
         9   0.9662 0.9255         

The top 5 variables (out of 6):
   USize, UShap, BNucl, Chrom, Epith

You can get the optimal variables:

bayesProfile$optVariables
[1] "USize" "UShap" "BNucl" "Chrom" "Epith" "Thick"
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