Bayes Network

library ("bnlearn") # example using learning.test data set
tan = tree.bayes (ac, "A")
tan$arcs # graph structure, arcs
plot(tan)
fitted = bn.fit(tan, ac, method = "bayes")
pred = predict(fitted, ac)
ACC <- sum(diag(table(pred, ac[,"A"])))/sum((table(pred, ac[,"A"])))

learning.test for 5000 observation

## Warning: package 'bnlearn' was built under R version 4.0.5

##   A B C D E F
## 1 b c b a b b
## 2 b a c a b b
## 3 a a a a a a
## 4 a a a a b b
## 5 a a b c a a
## 6 c c a c c a

fitted = bn.fit(tan, d1, method = "bayes")
pred = predict(fitted, d1)
ACC_d1 <- sum(diag(table(pred, d1[,"A"])))/sum((table(pred, d1[,"A"])))
ACC_d1

## [1] 0.7764

learning.test for 3000 observation

fitted = bn.fit(tan, d2, method = "bayes")
pred = predict(fitted, d2)
ACC_d2 <- sum(diag(table(pred,d2[,"A"])))/sum((table(pred,d2[,"A"])))
ACC_d2

## [1] 0.7793333

learning.test for 500 observation

fitted = bn.fit(tan, d3, method = "bayes")
pred = predict(fitted, d3)
ACC_d3 <- sum(diag(table(pred, d3[,"A"])))/sum((table(pred, d3[,"A"])))
ACC_d3

## [1] 0.794