e1071 package-naive bayes classifier di R menggunakan data iris
pengertian
pengertian naive bayes
Naïve bayes merupakan salah satu algoritma yang ada pada Teknik
klasifikasi, menggunakan metode probabilitas dan statistik.
Naïve bayes biasa digunkan untuk memprediksi peluang dimasa depan
berdasarkan pengalaman pada masa sebelumnya. Algoritma Naïve Bayes
merupakan metode yang ditemukan oleh ilmuan inggris yaitu Thomas Bayes,
sehingga dikenal theorema naïve bayes.
e1071 package
Fungsi paket e1071 meliputi analisis kelas laten, transformasi Fourier waktu singkat, pengelompokan fuzzy, mesin vektor pendukung, komputasi jalur terpendek, pengelompokan kantong, pengklasifikasi naif Bayes…
Tahapan e1071 package
naive bayes tutorial
install the package (e1071)
jika package e1071 belum ada maka bisa meng-installnya pada packages dan install.
library(e1071)test data simpel
lalu cek data yang akan digunakan, kita akan menggunkan data “iris”
#quick summary and plots
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 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 5.0 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 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicairis## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 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 5.0 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 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicasummary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## test sederhana
table(predict(classifier, iris[1]), iris[,5], dnn=list('predicted','actual'))## Warning in predict.naiveBayes(classifier, iris[1]): Type mismatch between
## training and new data for variable 'Sepal.Width'. Did you use factors with
## numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(classifier, iris[1]): Type mismatch between
## training and new data for variable 'Petal.Length'. Did you use factors with
## numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(classifier, iris[1]): Type mismatch between
## training and new data for variable 'Petal.Width'. Did you use factors with
## numeric labels for training, and numeric values for new data?## actual
## predicted setosa versicolor virginica
## setosa 45 6 1
## versicolor 5 33 18
## virginica 0 11 31model naive bayes iris dengan R
prosess
library “caret”
gunakan library (caret)
library(caret)## Loading required package: ggplot2## Loading required package: latticetampilkan data
kita dapat menampilkan semua data dengan iris seperti cara awal atau menggunakan command “names” untuk menunjukan label/nama kolom.
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 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 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosanames(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"x = iris[,-5]
y = iris$SpeciesDalam hal ini ditetapkan data dari kolom 1-4 (fitur) ke variabel x, dan kelas ke variabel y
model = train(x,y,'nb',trControl=trainControl(method='cv',number=10))Tampilkan model ringkasan, cukup ketik “model”
model## Naive Bayes
##
## 150 samples
## 4 predictor
## 3 classes: 'setosa', 'versicolor', 'virginica'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 135, 135, 135, 135, 135, 135, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.9533333 0.93
## TRUE 0.9600000 0.94
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.gunakan prediksi untuk mendapatkan nilai prediksi, dan kelas hasil:
predict(model$finalModel,x)## $class
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor virginica versicolor
## [73] versicolor versicolor versicolor versicolor versicolor virginica
## [79] versicolor versicolor versicolor versicolor versicolor virginica
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica versicolor virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica versicolor
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica versicolor virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
##
## $posterior
## setosa versicolor virginica
## [1,] 1.000000e+00 3.122328e-09 8.989129e-11
## [2,] 9.999999e-01 4.953302e-08 1.361560e-09
## [3,] 1.000000e+00 1.949717e-08 1.152761e-09
## [4,] 1.000000e+00 1.146273e-08 6.616756e-10
## [5,] 1.000000e+00 8.839954e-10 8.567477e-11
## [6,] 1.000000e+00 3.818715e-09 5.965843e-09
## [7,] 1.000000e+00 7.394006e-09 6.702907e-10
## [8,] 1.000000e+00 5.311568e-09 1.920277e-10
## [9,] 1.000000e+00 6.502476e-09 3.193962e-10
## [10,] 9.999998e-01 1.731985e-07 5.531788e-09
## [11,] 1.000000e+00 1.233528e-09 4.372981e-10
## [12,] 1.000000e+00 6.936685e-09 4.552987e-10
## [13,] 9.999998e-01 2.398420e-07 8.627082e-09
## [14,] 9.999999e-01 1.000884e-07 5.966595e-09
## [15,] 1.000000e+00 1.005497e-08 1.606871e-08
## [16,] 1.000000e+00 2.409589e-08 1.559800e-09
## [17,] 1.000000e+00 2.067535e-09 3.230036e-09
## [18,] 1.000000e+00 1.262295e-08 3.634125e-10
## [19,] 9.999999e-01 1.082521e-08 6.920719e-08
## [20,] 1.000000e+00 2.085870e-10 4.809744e-10
## [21,] 9.999999e-01 9.294094e-08 2.352315e-09
## [22,] 1.000000e+00 1.160122e-09 3.869756e-10
## [23,] 1.000000e+00 4.305256e-09 1.046271e-09
## [24,] 9.999988e-01 1.208288e-06 2.529554e-08
## [25,] 1.000000e+00 3.485480e-08 2.287742e-09
## [26,] 9.999999e-01 8.860267e-08 1.755425e-09
## [27,] 1.000000e+00 3.615611e-08 1.307142e-09
## [28,] 1.000000e+00 5.101289e-09 1.045515e-10
## [29,] 1.000000e+00 1.096715e-08 1.859280e-10
## [30,] 1.000000e+00 1.882032e-08 1.112743e-09
## [31,] 1.000000e+00 4.348022e-08 1.817230e-09
## [32,] 9.999999e-01 1.214791e-07 3.074610e-09
## [33,] 1.000000e+00 1.234324e-09 6.099208e-11
## [34,] 1.000000e+00 1.317063e-09 4.679792e-11
## [35,] 1.000000e+00 3.070357e-08 9.806416e-10
## [36,] 9.999999e-01 9.546561e-08 2.581158e-09
## [37,] 1.000000e+00 3.790672e-08 1.660847e-09
## [38,] 1.000000e+00 4.598664e-09 6.183595e-10
## [39,] 1.000000e+00 6.577049e-09 3.820588e-10
## [40,] 1.000000e+00 6.711099e-09 1.598209e-10
## [41,] 1.000000e+00 1.707312e-08 7.461967e-10
## [42,] 9.999997e-01 3.444123e-07 3.593167e-09
## [43,] 1.000000e+00 2.420471e-09 1.918806e-10
## [44,] 9.999999e-01 1.178364e-07 5.150149e-09
## [45,] 1.000000e+00 1.764715e-09 4.069202e-09
## [46,] 9.999998e-01 1.718906e-07 6.182882e-09
## [47,] 1.000000e+00 8.512019e-11 1.962761e-10
## [48,] 1.000000e+00 6.805542e-09 4.613946e-10
## [49,] 1.000000e+00 8.054099e-10 2.034324e-10
## [50,] 1.000000e+00 1.082759e-08 3.441172e-10
## [51,] 5.772566e-08 8.752420e-01 1.247580e-01
## [52,] 2.167337e-08 9.440564e-01 5.594360e-02
## [53,] 3.568954e-08 6.419224e-01 3.580775e-01
## [54,] 6.349255e-07 9.999899e-01 9.468672e-06
## [55,] 2.765029e-09 9.426707e-01 5.732934e-02
## [56,] 3.434654e-07 9.982960e-01 1.703625e-03
## [57,] 5.439706e-08 6.776127e-01 3.223873e-01
## [58,] 3.468079e-05 9.999651e-01 2.572784e-07
## [59,] 4.948922e-09 9.852152e-01 1.478477e-02
## [60,] 2.291638e-06 9.999811e-01 1.663397e-05
## [61,] 1.974462e-05 9.999802e-01 4.396638e-08
## [62,] 6.261483e-07 9.970784e-01 2.920926e-03
## [63,] 8.587179e-08 9.999991e-01 8.412559e-07
## [64,] 1.796146e-08 9.675910e-01 3.240896e-02
## [65,] 2.894394e-06 9.999809e-01 1.619585e-05
## [66,] 1.569871e-08 9.826467e-01 1.735332e-02
## [67,] 1.925716e-06 9.931938e-01 6.804227e-03
## [68,] 2.775053e-07 9.999949e-01 4.831298e-06
## [69,] 1.406347e-09 9.942356e-01 5.764440e-03
## [70,] 2.791581e-07 9.999989e-01 8.655960e-07
## [71,] 3.910445e-06 1.891683e-01 8.108278e-01
## [72,] 9.899047e-09 9.998136e-01 1.863719e-04
## [73,] 7.103526e-10 8.374831e-01 1.625169e-01
## [74,] 1.303322e-08 9.954267e-01 4.573304e-03
## [75,] 4.382054e-09 9.969695e-01 3.030520e-03
## [76,] 8.746457e-09 9.849134e-01 1.508656e-02
## [77,] 4.579961e-09 9.007918e-01 9.920822e-02
## [78,] 1.487415e-08 9.857797e-02 9.014220e-01
## [79,] 1.024339e-07 9.846645e-01 1.533541e-02
## [80,] 4.265702e-07 9.999991e-01 4.841056e-07
## [81,] 1.300151e-06 9.999985e-01 1.821381e-07
## [82,] 1.807807e-06 9.999981e-01 9.431746e-08
## [83,] 2.331858e-07 9.999877e-01 1.207643e-05
## [84,] 8.810516e-08 4.872692e-01 5.127307e-01
## [85,] 5.328649e-06 9.965100e-01 3.484697e-03
## [86,] 2.941491e-06 9.183115e-01 8.168560e-02
## [87,] 1.889516e-08 8.644605e-01 1.355395e-01
## [88,] 1.131003e-09 9.989977e-01 1.002314e-03
## [89,] 1.884696e-06 9.998468e-01 1.512839e-04
## [90,] 1.943757e-07 9.999724e-01 2.737753e-05
## [91,] 1.838186e-07 9.998321e-01 1.677539e-04
## [92,] 2.683750e-08 9.784001e-01 2.159988e-02
## [93,] 7.517141e-08 9.999722e-01 2.768981e-05
## [94,] 4.864283e-05 9.999513e-01 1.026397e-07
## [95,] 2.184448e-07 9.997510e-01 2.488203e-04
## [96,] 1.935069e-06 9.998496e-01 1.485057e-04
## [97,] 7.829663e-07 9.996389e-01 3.602814e-04
## [98,] 3.522009e-09 9.978893e-01 2.110726e-03
## [99,] 2.626926e-05 9.999726e-01 1.169950e-06
## [100,] 4.187270e-07 9.998386e-01 1.609416e-04
## [101,] 8.943071e-08 1.262441e-06 9.999986e-01
## [102,] 4.982326e-07 2.784325e-02 9.721562e-01
## [103,] 2.383733e-08 3.190082e-07 9.999997e-01
## [104,] 5.405655e-09 2.070573e-04 9.997929e-01
## [105,] 1.008303e-08 5.706519e-07 9.999994e-01
## [106,] 9.293978e-08 1.235171e-08 9.999999e-01
## [107,] 7.722932e-06 9.539952e-01 4.599708e-02
## [108,] 4.012792e-08 3.420777e-05 9.999658e-01
## [109,] 1.102340e-09 1.316479e-04 9.998684e-01
## [110,] 1.061163e-06 1.288890e-07 9.999988e-01
## [111,] 1.606940e-08 4.582301e-04 9.995418e-01
## [112,] 1.099040e-09 2.028028e-03 9.979720e-01
## [113,] 1.219887e-08 4.522242e-06 9.999955e-01
## [114,] 6.716778e-07 9.692655e-03 9.903067e-01
## [115,] 1.995818e-06 9.821642e-04 9.990158e-01
## [116,] 1.797516e-08 3.245108e-05 9.999675e-01
## [117,] 7.871035e-09 6.025113e-04 9.993975e-01
## [118,] 8.035266e-07 4.610216e-11 9.999992e-01
## [119,] 1.114633e-08 1.821720e-08 1.000000e+00
## [120,] 1.021622e-07 9.462047e-01 5.379522e-02
## [121,] 3.122868e-08 3.545649e-07 9.999996e-01
## [122,] 5.288522e-06 1.517350e-02 9.848212e-01
## [123,] 3.582523e-08 4.116272e-08 9.999999e-01
## [124,] 1.441158e-09 8.278185e-02 9.172181e-01
## [125,] 3.394413e-08 3.174989e-07 9.999996e-01
## [126,] 5.596750e-08 2.185371e-05 9.999781e-01
## [127,] 4.673576e-09 1.622147e-01 8.377853e-01
## [128,] 5.947352e-08 1.003789e-01 8.996210e-01
## [129,] 3.101469e-09 1.442934e-06 9.999986e-01
## [130,] 6.122836e-08 3.400634e-04 9.996599e-01
## [131,] 1.389474e-08 2.976597e-06 9.999970e-01
## [132,] 8.766738e-07 2.312170e-10 9.999991e-01
## [133,] 3.229494e-09 1.502497e-06 9.999985e-01
## [134,] 5.830326e-09 5.463686e-01 4.536314e-01
## [135,] 2.147851e-08 2.848673e-02 9.715132e-01
## [136,] 4.877334e-08 3.680347e-09 9.999999e-01
## [137,] 6.367711e-08 9.930942e-07 9.999989e-01
## [138,] 1.041818e-08 5.448493e-04 9.994551e-01
## [139,] 5.004383e-07 2.024076e-01 7.975919e-01
## [140,] 1.996159e-08 1.165611e-05 9.999883e-01
## [141,] 1.999575e-08 1.295204e-06 9.999987e-01
## [142,] 2.161884e-08 1.231467e-04 9.998768e-01
## [143,] 4.982326e-07 2.784325e-02 9.721562e-01
## [144,] 3.349443e-08 4.857252e-07 9.999995e-01
## [145,] 7.668677e-08 7.172953e-07 9.999992e-01
## [146,] 1.158419e-08 8.914668e-05 9.999108e-01
## [147,] 7.841053e-10 2.133359e-02 9.786664e-01
## [148,] 7.966669e-09 3.437503e-04 9.996562e-01
## [149,] 5.761966e-08 1.351426e-05 9.999864e-01
## [150,] 1.368634e-06 5.710392e-02 9.428947e-01yang terakhir, kita perlu mengetahui berapa banyak kesalahan yang diklasifikasikan, sehingga kita perlu membandingkan hasil prediksi dengan kelas/spesies iris.
table(predict(model$finalModel,x)$class,y)## y
## setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 47 3
## virginica 0 3 47library(klaR)## Loading required package: MASSnaive_iris <- NaiveBayes(iris$Species ~ ., data = iris)
plot(naive_iris)
jika ingin menampilkan plot dapat menggunakan command diatas