• 安裝package：
• 導入e1071：
• 找一個數(shù)據(jù)集：
• 訓練并查看訓練結(jié)果：
• 下面看一下，這個庫如何處理標稱型特征：
• 補充一下，如果某個數(shù)據(jù)缺少某些特征：
• 參考：

安裝package：

?

1	> install.packages("e1071")

導入e1071：

?

1	> library(e1071)

找一個數(shù)據(jù)集：

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153

> data(iris) > 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? virginica

Sepal意思是“花萼 ”，Petal意思是“ 花瓣”。很明顯，前四列是花萼和花瓣的特征，第五列代表相應的分類。我們可以用這個數(shù)據(jù)集進行貝葉斯訓練。?

先看一下，對這個數(shù)據(jù)集summary的結(jié)果：?
?

1 2 3 4 5 6 7 8

> summary(iris) ??Sepal.Length??? Sepal.Width???? Petal.Length??? Petal.Width????????? Species? ?Min.?? :4.300?? Min.?? :2.000?? Min.?? :1.000?? Min.?? :0.100?? setosa??? :50? ?1st Qu.:5.100?? 1st Qu.:2.800?? 1st Qu.:1.600?? 1st Qu.:0.300?? versicolor:50? ?Median :5.800?? Median :3.000?? Median :4.350?? Median :1.300?? virginica :50? ?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

訓練并查看訓練結(jié)果：

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

> classifier<-naiveBayes(iris[,1:4], iris[,5]) > classifier Naive Bayes Classifier for Discrete Predictors Call: naiveBayes.default(x = iris[, 1:4], y = iris[, 5]) A-priori probabilities: iris[, 5] ????setosa versicolor? virginica ?0.3333333? 0.3333333? 0.3333333 Conditional probabilities: ????????????Sepal.Length iris[, 5]???? [,1]????? [,2] ??setosa???? 5.006 0.3524897 ??versicolor 5.936 0.5161711 ??virginica? 6.588 0.6358796 ????????????Sepal.Width iris[, 5]???? [,1]????? [,2] ??setosa???? 3.428 0.3790644 ??versicolor 2.770 0.3137983 ??virginica? 2.974 0.3224966 ????????????Petal.Length iris[, 5]???? [,1]????? [,2] ??setosa???? 1.462 0.1736640 ??versicolor 4.260 0.4699110 ??virginica? 5.552 0.5518947 ????????????Petal.Width iris[, 5]???? [,1]????? [,2] ??setosa???? 0.246 0.1053856 ??versicolor 1.326 0.1977527 ??virginica? 2.026 0.2746501 > classifier$apriori iris[, 5] ????setosa versicolor? virginica ????????50???????? 50???????? 50 > classifier$tables $Sepal.Length ????????????Sepal.Length iris[, 5]???? [,1]????? [,2] ??setosa???? 5.006 0.3524897 ??versicolor 5.936 0.5161711 ??virginica? 6.588 0.6358796 $Sepal.Width ????????????Sepal.Width iris[, 5]???? [,1]????? [,2] ??setosa???? 3.428 0.3790644 ??versicolor 2.770 0.3137983 ??virginica? 2.974 0.3224966 $Petal.Length ????????????Petal.Length iris[, 5]???? [,1]????? [,2] ??setosa???? 1.462 0.1736640 ??versicolor 4.260 0.4699110 ??virginica? 5.552 0.5518947 $Petal.Width ????????????Petal.Width iris[, 5]???? [,1]????? [,2] ??setosa???? 0.246 0.1053856 ??versicolor 1.326 0.1977527 ??virginica? 2.026 0.2746501

classifier中：?
?

1 2 3 4

A-priori probabilities: iris[, 5] ????setosa versicolor? virginica ?0.3333333? 0.3333333? 0.3333333

很好理解，就是類別的先驗概率。?
而：?
?

1 2 3 4 5 6

$Petal.Width ????????????Petal.Width iris[, 5]???? [,1]????? [,2] ??setosa???? 0.246 0.1053856 ??versicolor 1.326 0.1977527 ??virginica? 2.026 0.2746501

是特征Petal.Width的條件概率，在這個貝葉斯實現(xiàn)中，特征是數(shù)值型數(shù)據(jù)（而且還還有小數(shù)部分），這里假設概率密度符合高斯分布。比如對于特征Petal.Width，其屬于setosa的概率符合mean為0.246，標準方差為0.1053856的高斯分布。?



預測：?
預測iris數(shù)據(jù)集中的第一個數(shù)據(jù)：?
?

1 2 3

> predict(classifier, iris[1, -5]) [1] setosa Levels: setosa versicolor virginica

iris[1,-5]表示第一行的前4列。

看一下該分類器的效果：

?

1 2 3 4 5 6

> table(predict(classifier, iris[,-5]), iris[,5], dnn=list('predicted','actual')) ????????????actual predicted??? setosa versicolor virginica ??setosa???????? 50????????? 0???????? 0 ??versicolor????? 0???????? 47???????? 3 ??virginica?????? 0????????? 3??????? 47

分類效果還是不錯的。

自己構(gòu)造一個新的數(shù)據(jù)并預測：?
?

1 2 3 4

> new_data = data.frame(Sepal.Length=7, Sepal.Width=3, Petal.Length=6, Petal.Width=2) > predict(classifier, new_data) [1] virginica Levels: setosa versicolor virginica

如果少一個特征（只有三個特征）：

?

1 2 3 4

> new_data = data.frame(Sepal.Length=7, Sepal.Width=3, Petal.Length=6) > predict(classifier, new_data) [1] virginica Levels: setosa versicolor virginica

下面看一下，這個庫如何處理標稱型特征：

數(shù)據(jù)如下：?
?

1 2 3 4 5 6 7 8 9 10 11 12 13 14

> model = c("H", "H", "H", "H", "T", "T", "T", "T") > place = c("B", "B", "N", "N", "B", "B", "N", "N") > repairs = c("Y", "N", "Y", "N", "Y", "N", "Y", "N") > dataset = data.frame(model, place, repairs) > dataset ??model place repairs 1???? H???? B?????? Y 2???? H???? B?????? N 3???? H???? N?????? Y 4???? H???? N?????? N 5???? T???? B?????? Y 6???? T???? B?????? N 7???? T???? N?????? Y 8???? T???? N?????? N

貝葉斯之：?
?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

> classifier<-naiveBayes(dataset[,1:2], dataset[,3]) > classifier Naive Bayes Classifier for Discrete Predictors Call: naiveBayes.default(x = dataset[, 1:2], y = dataset[, 3]) A-priori probabilities: dataset[, 3] ??N?? Y 0.5 0.5 Conditional probabilities: ????????????model dataset[, 3]?? H?? T ???????????N 0.5 0.5 ???????????Y 0.5 0.5 ????????????place dataset[, 3]?? B?? N ???????????N 0.5 0.5 ???????????Y 0.5 0.5

好了，預測一下：?
?

1 2 3 4

> new_data = data.frame(model="H", place="B") > predict(classifier, new_data) [1] N Levels: N Y

perfect！?

補充一下，如果某個數(shù)據(jù)缺少某些特征：

可以用NA代替該特征：

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

> model = c("H", "H", "H", "H", "T", "T", "T", "T") > place = c("B", "B", "N", "N", "B", "B", NA, NA) > repairs = c("Y", "N", "Y", "N", "Y", "N", "Y", "N") > dataset = data.frame(model, place, repairs) > dataset ??model place repairs 1???? H???? B?????? Y 2???? H???? B?????? N 3???? H???? N?????? Y 4???? H???? N?????? N 5???? T???? B?????? Y 6???? T???? B?????? N 7???? T? <NA>?????? Y 8???? T? <NA>?????? N > classifier<-naiveBayes(dataset[,1:2], dataset[,3]) > classifier Naive Bayes Classifier for Discrete Predictors Call: naiveBayes.default(x = dataset[, 1:2], y = dataset[, 3]) A-priori probabilities: dataset[, 3] ??N?? Y 0.5 0.5 Conditional probabilities: ????????????model dataset[, 3]?? H?? T ???????????N 0.5 0.5 ???????????Y 0.5 0.5 ????????????place dataset[, 3]???????? B???????? N ???????????N 0.6666667 0.3333333 ???????????Y 0.6666667 0.3333333

參考：

http://www-users.cs.york.ac.uk/~jc/teaching/arin/R_practical/
http://pythonhosted.org//NaiveBayes/

總結(jié)

以上是生活随笔為你收集整理的R Bayes的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。