The document introduces the author as a user of R with the twitter handle nobunari03. It then provides an example of using the Naive Bayes algorithm in R to classify examples based on attributes like 'bast', 'west', and 'hip' using training and test data from files. Graphs are generated to visualize the Naive Bayes model and an example prediction is made on the test data to classify examples based on the attribute values.

1. PC

2. Who am I?
• twitter:nobunari03
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3. 1.
2.R

4. PC

8. •

9. R

10. R
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http://www.r-project.org/

11. Why R?
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12. 1.PC
2.PC
PC

14. AV

17. Code
library(klaR) #klaR
ero.training <- read.csv("ero.csv")
Training( ) Test( )!
ero.test <- read.csv("idol.csv")
mN <- NaiveBayes(kawaisa~ bast + west +
hip,data=ero.training)
plot(mN)

19. Bast
Naive Bayes Plot
no
0.05
yes
0.04
Density
0.03
0.02
0.01
0.00
85 90 95 100
bast

20. West Naive Bayes Plot
0.15
no
yes
0.10
Density
0.05
0.00
56 58 60 62 64 66
west

21. Hip
Naive Bayes Plot
0.15
no
yes
0.10
Density
0.05
0.00
84 86 88 90 92 94
hip

22. AV
90
59
87
” ”

23. mNpred <-
predict(mN,ero.test[,2:5])
table(ero.test$Name,mNpred
$class)

25. Demo
no yes
1 0
0 1
1 0
0 1
0 1
1 0

26. B:91 W:60 H:87 B: 88W:61 H:89 B:83 W:60 H:79
B:85 W:55 H:84 B:88 W:56H:87 B:88 W:58 H:84
90
59
87

27. •

28. R
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