metode holout dan stratified cv

8/17/2019 Metode holout dan stratified cv

1/8

Data Mining

Kelompok 3 :

1. Farida Apriani 13152010262. Hidayatul Khuna 1315201031

A. Hail untuk random ketiga

1. Data Testing 10, Training 90

Observasi Predicted

Total0 1

0 47 6 53

1 11 13 24

58 19 77

Akurasi=(TP+TN )

ALL =

(47+13)77

=0.78

Apper=1−(TP+TN )

ALL =1−

(47+13)77

=1−0.78=0.22

Sensitifity=TP

P

=13

24=0.54

Specificity=TN

N =

47

53=0.89


Observasi Predicted

Total0 1

0 86 11 971 25 32 57

111 43 154

Akurasi=(TP+TN )

ALL =

(86+32)154

=0.77

Apper=1−(TP+TN )

ALL =1−

(86+32)154

=1−0.77=0.23

1


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Data Mining

Sensitifity=TP

P =

32

57=0.56

Specificity=TN

N =

86

97=0.89


Observasi Predicted

Total0 1

0 119 20 139

1 36 55 91155 75 230

Akurasi=(TP+TN )

ALL =

(119+55)230

=0.76

Apper=1−(TP+TN )

ALL =1−

(119+55)230

=1−0.76=0.24

Sensitifity=TP

P =

55

91=0.60

Specificity=TN

N =

119

139=0.86


Observasi Predicted

Total0 1

0 157 25 180

1 52 73 125

209 96 305

Akurasi=(TP+TN )

ALL =

(157+73)307

=0.75

2


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Data Mining

Apper=1−(TP+TN )

ALL =1−

(439+161)768

=1−0.78=0.22

Sensitifity=

TP

P =

161

268=0.60

Specificity=TN

N =

439

500=0.88

!. Hail untuk random ketiga


Observasi Predicted

0 1

0 50 3 53

1 11 13 2461 26 77

Akurasi=(TP+TN )

ALL =

(50+13)77

=0.82

Apper=1−(TP+TN )

ALL =1−

(50+13)77

=1−0.82=0.18

Sensitifity=TP

P =

13

24=0.54

Specificity=TN

N =

50

53=0.94


Observasi Predicted0 1

"


5/8


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Data Mining

0 180 19 199

1 42 66 108

222 85 307

Akurasi=(TP+TN )

ALL =

(180+66)307

=0.8

Apper=1−(TP+TN )

ALL =1−

(180+66)307

=1−0.8=0.2

Sensitifity=TP

P =

66

108=0.61

Specificity=TN

N =

180

199=0.9


Observasi Predicted

0 1

0 220 29 249

1 52 83 136272 112 384

Akurasi=(TP+TN )

ALL =

(220+83)384

=0.79

Apper=1−(TP+TN )

ALL =1−

(220+83)384

=1−0.79=0.21

Sensitifity=TP

P =

83

136=0.61

Specificity=TN

N =

220

249=0.88

6. ross !alidation dengan "old 5

Observasi Predicted0 1

6


7/8

Data Mining

0 441 59 500

1 106 162 268

547 221 768

Akurasi=(TP+TN )

ALL =

(441+162)768

=0.78

Apper=1−(TP+TN )

ALL =1−

(441+162)768

=1−0.78=0.22

Sensitifity=TP

P =

162

268=0.6

Specificity=TN

N =

441

500=0.88

#. Keimpulan

Dengan proedur yang ama$ dilakukan klai%kai menggunakan

random pertama hingga kelima kemudian dari hail klai%kai tere&ut

dihitung 'arina maing(maing random ehingga diperoleh keimpulan

e&agai &erikut.

Training 0.9 0.8 0.7 0.6 0.5

ross#

!alidation

)arian

Testing 0.1 0.2 0.3 0.4 0.5

#ro)alidation

$%&rasi

1 0.79 0.82 0.82 0.79 0.80 0.790.000163*

6*+.+*+0",(06

2 0.77 0.78 0.79 0.78 0.79 0.78-.031*2,(

053 0.78 0.77 0.76 0.75 0.75 0.78 0.0001*

4 0.71 0.78 0.80 0.78 0.79 0.78

0.00116-*

-+5 0.82 0.81 0.80 0.80 0.79 0.78 0.00013

'ensiti(it)

1 0.56 0.61 0.63 0.60 0.64 0.610.000+5*"

-1*.2+""*,(05

2 0.54 0.51 0.53 0.55 0.59 0.590.000+0+5

-

3 0.54 0.56 0.60 0.58 0.58 0.60 0.00052

4 0.54 0.64 0.66 0.59 0.60 0.590.0023260

++

5 0.54 0.56 0.63 0.61 0.61 0.60 0.001"5'*eci(icit)

1 0.90 0.91 0.91 0.90 0.88 0.880.0001+10

*+ 6.+,(06

2 0.88 0.93 0.93 0.91 0.90 0.89 0.000"3020+

*


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Data Mining

3 0.89 0.89 0.86 0.86 0.85 0.88 0.00035

4 0.80 0.85 0.87 0.88 0.88 0.880.00101-*

065 0.94 0.92 0.90 0.90 0.88 0.88 0.00052

$P+

1 0.21 0.18 0.18 0.21 0.20 0.210.0001+16

5

+.+*+0

",(062 0.23 0.22 0.21 0.22 0.21 0.22

*."30*6,(05

3 0.22 0.23 0.24 0.25 0.25 0.22 0.0001-

4 0.29 0.22 0.20 0.22 0.21 0.220.000-""-

2+

5 0.18 0.19 0.20 0.20 0.21 0.22 0.0002

Dari ta&el diata 'arian terkeil untuk akurai terdapat pada random

ke 2$ eniti%ty pada random ke 3$ pei%ity ada random ke 1$ A/, pada

random ke 2. ntuk #ro )alidation 'arian terkeil terdapat pada

pei%ity

+

metode holout dan stratified cv

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