Data Mining and Machine Learning

Decision tree using the Wine type data set

University

R version 3.1.0 (2014-04-10) -- "Spring Dance"

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> library (tree)

Warning message:

package ‘tree’ was built under R version 3.1.1

> library (ISLR)

> mywinetype <- read.csv(url("http://uopeopleweb.com/winetype.csv"), header=TRUE)

> attach(mywinetype)

> names(mywinetype)

[1] "Class" "Alcohol" "Malic_acid"

[4] "Ash" "Alcalinity_of_ash" "Magnesium"

[7] "Total_phenols" "Flavanoids" "Nonflavanoid_phenols"

[10] "Proanthocyanins" "Color_intensity" "Hue"

[13] "diluted_wines" "Proline"

> dim(mywinetype)

[1] 178 14

> attributes(mywinetype)

$names

[1] "Class" "Alcohol" "Malic_acid"

[4] "Ash" "Alcalinity_of_ash" "Magnesium"

[7] "Total_phenols" "Flavanoids" "Nonflavanoid_phenols"

[10] "Proanthocyanins" "Color_intensity" "Hue"

[13] "diluted_wines" "Proline"

$class

[1] "data.frame"

$row.names

[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

[19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

[37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

[55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

[73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90

[91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108

[109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126

[127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144

[145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162

[163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178

> head(mywinetype)

Class Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium Total_phenols

1 1 14.23 1.71 2.43 15.6 127 2.80

2 1 13.20 1.78 2.14 11.2 100 2.65

3 1 13.16 2.36 2.67 18.6 101 2.80

4 1 14.37 1.95 2.50 16.8 113 3.85

5 1 13.24 2.59 2.87 21.0 118 2.80

6 1 14.20 1.76 2.45 15.2 112 3.27

Flavanoids Nonflavanoid_phenols Proanthocyanins Color_intensity Hue

1 3.06 0.28 2.29 5.64 1.04

2 2.76 0.26 1.28 4.38 1.05

3 3.24 0.30 2.81 5.68 1.03

4 3.49 0.24 2.18 7.80 0.86

5 2.69 0.39 1.82 4.32 1.04

6 3.39 0.34 1.97 6.75 1.05

diluted_wines Proline

1 3.92 1065

2 3.40 1050

3 3.17 1185

4 3.45 1480

5 2.93 735

6 2.85 1450

> winetype <- tree(Class ~ Malic_acid + Ash + Alcalinity_of_ash + Magnesium + Total_phenols + Flavanoids + Nonflavanoid_phenols + Proanthocyanins + Color_intensity + Hue + diluted_wines + Proline, data=mywinetype)

> summary(winetype)

Regression tree:

tree(formula = Class ~ Malic_acid + Ash + Alcalinity_of_ash +

Magnesium + Total_phenols + Flavanoids + Nonflavanoid_phenols +

Proanthocyanins + Color_intensity + Hue + diluted_wines +

Proline, data = mywinetype)

Variables actually used in tree construction:

[1] "Flavanoids" "Color_intensity" "Proline"

Number of terminal nodes: 5

Residual mean deviance: 0.01596 = 2.761 / 173

Distribution of residuals:

Min. 1st Qu. Median Mean 3rd Qu. Max.

-0.98150 0.00000 0.01852 0.00000 0.02041 0.20000

> plot(winetype)

> text(winetype)

> winetype

node), split, n, deviance, yval

* denotes terminal node

1) root 178 106.3000 1.938

2) Flavanoids < 1.575 62 10.8400 2.774

4) Color_intensity < 3.825 13 0.0000 2.000 *

5) Color_intensity > 3.825 49 0.9796 2.980 *

3) Flavanoids > 1.575 116 28.9900 1.491

6) Proline < 724.5 54 0.9815 1.981 *

7) Proline > 724.5 62 3.7420 1.065

14) Color_intensity < 3.55 5 0.8000 1.800 *

15) Color_intensity > 3.55 57 0.0000 1.000 *