PowerPoint 프레젠테이션 - Kangwon

PowerPoint 프레젠테이션 - Kangwon

2014 ([email protected]) : (Classification) Classification) ) ( : ) : , (Classification) ) . : . : (Classification) , ) 2 Decision) Trees 3

Example 1: Iris Datasets: 150 Iris Sepal.Len) gth: Sepal.Width: Petal.Len) gth: Petal.Width: Species: setosa versicolor virgin) ica Sepal.Leng Sepal.Wid Petal.Leng Petal.Wid Specie th th th th s 1 5.1 3.5 1.4

0.2 setosa 2 4.9 3 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5 3.6 1.4 0.2 setosa 6

5.4 3.9 1.7 0.4 setosa 7 4.6 3.4 1.4 0.3 setosa 8 5 3.4 1.5 0.2 setosa 9 4.4 2.9 1.4 0.2 setosa 10 4.9 3.1

1.5 0.1 setosa 11 5.4 3.7 1.5 0.2 setosa 12 4.8 3.4 1.6 0.2 setosa 13 4.8 3 1.4 0.1 setosa 14 4.3 3 1.1 0.1 setosa

15 5.8 4 1.2 0.2 setosa # Iris ? 4 Ex.1: Decision) Tree http://cran) .r-project.org/web/packages/party/in) dex.html http://cran) .r-project.org/web/packages/zoo/in) dex.html http://cran) .r-project.org/web/packages/san) dwich/in) dex.html http://cran) .r-project.org/web/packages/strucchan) ge/in) dex.html http://cran) .r-project.org/web/packages/modeltools/in) dex.html http://cran) .r-project.org/web/packages/coin) /in) dex.html http://cran) .r-project.org/web/packages/mvtn) orm/in) dex.html R

5 Ex.1: R iris 6 Ex.1: sample(Classification) ) 70% 30% replace=TRUE: prob=c(Classification) 0.7, 0.3): 7 Ex.1: Decision) Tree ctree(Classification) ) : Decision) Tree 8

Ex.1: Decision) Tree Decision) Tree 9 Ex.1: Decision) Tree Decision) Tree 10 Ex.1: Decision) Tree predict(Classification) ) : 11 The k-Nearest Neighbor Algorithm kn) n) (Classification) : )

k Euclidean) distan) ce ? A 3 104

20.5 B 2 100 18.7 C 1 81 19.2

D 101 10 115.3 E 99 5 117.4 F

98 2 118.9 ? 18 90 (Classification) ) k=4 3 , 1 ? 12 Example 2: Foren) sic Glass Datasets: 6 214

Win) F: float glass win) dow # RI 1 3.01 13.64 4.49 Win) NF: n) on) -float glass win) dow Na Mg Al Si K

Ca Ba Fe type 1.1 71.78 0.06 8.75 0 0 Win) F 2 -0.39 13.89 3.6 1.36 72.73 0.48 7.83 0

0 Win) F 3 -1.82 13.53 3.55 1.54 72.99 0.39 7.78 0 0 Win) F Con) : con) tain) er (Classification) bottles) 4 -0.34 13.21 3.69 1.29 72.61 0.57 8.22 0 0 Win) F Tabl: Tableware

5 -0.58 13.27 3.62 1.24 73.08 0.55 8.07 0 0 Win) F Head: vehicle headlamp 6 -2.04 12.79 3.61 1.62 72.97 0.64 8.07 0 0.26 Win) F 7 -0.57 3.6 1.14 73.09 0.58 8.17

0 0 Win) F 8 -0.44 13.15 3.61 1.05 73.24 0.57 8.24 0 0 Win) F 0 0 Win) F Veh: vehicle win) dow

RI: (Classification) refractive in) dex) 9 13.3 1.18 14.04 3.58 1.37 72.08 0.56 8.3 Percen) tages of Na, Mg, Al, Si, K, Ca, Ba, an) d Fe type: ? 13 Ex.2: , http://cran) .r-project.org/web/packages/textir/in) dex.html http://cran) .r-project.org/web/packages/distrom/in) dex.html http://cran) .r-project.org/web/packages/gamlr/in) dex.html , library

R 14 Ex. 2: str(Classification) ) : 10 (Classification) RI, Percen) tages of elemen) ts, type) 214 15 Ex. 2: Box plots (Classification) 1/2) type box plot par(Classification) ) : par(mfrow=c(3,3), mai=c(.3,.6,.1,.1)) plot(RI ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Al ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Na ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Mg ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Ba ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Si ~ type, data=fgl, col=c(grey(.2),2:6))

plot(K ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Ca ~ type, data=fgl, col=c(grey(.2),2:6)) plot(Fe ~ type, data=fgl, col=c(grey(.2),2:6)) 16 Ex. 2: Box plots (Classification) 2/2) 17 Ex. 2: RIxAl (Classification) 200 ) (Classification) 14 ) n) t : sample(Classification) x, size, ) : x n) t 18 Ex. 2: RIxAl kNN

19 Ex. 2: RIxAl kNN plot plot(Classification) ) open) symbol poin) ts(Classification) ) solid symbol 20 Ex. 2: RIxAl kNN kNN type

1NN 78.6 , 5NN 71.4 21 #4 RI Al RI Percen) tages of Na, Mg, Al, Si, K, Ca, Ba, an) d Fe 214 (Classification) ) [email protected]) gwon) .ac.kr : [ ][ ]HW#4 , 20% 22

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