R语言朴素贝叶斯技术怎么使用

本篇内容主要讲解“R语言朴素贝叶斯技术怎么使用”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“R语言朴素贝叶斯技术怎么使用”吧!

安装package：

> install.packages("e1071")

导入e1071：

> library(e1071)

找一个数据集：

> data(iris)> iris    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species1            5.1         3.5          1.4         0.2     setosa2            4.9         3.0          1.4         0.2     setosa3            4.7         3.2          1.3         0.2     setosa4            4.6         3.1          1.5         0.2     setosa5            5.0         3.6          1.4         0.2     setosa6            5.4         3.9          1.7         0.4     setosa7            4.6         3.4          1.4         0.3     setosa8            5.0         3.4          1.5         0.2     setosa9            4.4         2.9          1.4         0.2     setosa10           4.9         3.1          1.5         0.1     setosa11           5.4         3.7          1.5         0.2     setosa12           4.8         3.4          1.6         0.2     setosa13           4.8         3.0          1.4         0.1     setosa14           4.3         3.0          1.1         0.1     setosa15           5.8         4.0          1.2         0.2     setosa16           5.7         4.4          1.5         0.4     setosa17           5.4         3.9          1.3         0.4     setosa18           5.1         3.5          1.4         0.3     setosa19           5.7         3.8          1.7         0.3     setosa20           5.1         3.8          1.5         0.3     setosa21           5.4         3.4          1.7         0.2     setosa22           5.1         3.7          1.5         0.4     setosa23           4.6         3.6          1.0         0.2     setosa24           5.1         3.3          1.7         0.5     setosa25           4.8         3.4          1.9         0.2     setosa26           5.0         3.0          1.6         0.2     setosa27           5.0         3.4          1.6         0.4     setosa28           5.2         3.5          1.5         0.2     setosa29           5.2         3.4          1.4         0.2     setosa30           4.7         3.2          1.6         0.2     setosa31           4.8         3.1          1.6         0.2     setosa32           5.4         3.4          1.5         0.4     setosa33           5.2         4.1          1.5         0.1     setosa34           5.5         4.2          1.4         0.2     setosa35           4.9         3.1          1.5         0.2     setosa36           5.0         3.2          1.2         0.2     setosa37           5.5         3.5          1.3         0.2     setosa38           4.9         3.6          1.4         0.1     setosa39           4.4         3.0          1.3         0.2     setosa40           5.1         3.4          1.5         0.2     setosa41           5.0         3.5          1.3         0.3     setosa42           4.5         2.3          1.3         0.3     setosa43           4.4         3.2          1.3         0.2     setosa44           5.0         3.5          1.6         0.6     setosa45           5.1         3.8          1.9         0.4     setosa46           4.8         3.0          1.4         0.3     setosa47           5.1         3.8          1.6         0.2     setosa48           4.6         3.2          1.4         0.2     setosa49           5.3         3.7          1.5         0.2     setosa50           5.0         3.3          1.4         0.2     setosa51           7.0         3.2          4.7         1.4 versicolor52           6.4         3.2          4.5         1.5 versicolor53           6.9         3.1          4.9         1.5 versicolor54           5.5         2.3          4.0         1.3 versicolor55           6.5         2.8          4.6         1.5 versicolor56           5.7         2.8          4.5         1.3 versicolor57           6.3         3.3          4.7         1.6 versicolor58           4.9         2.4          3.3         1.0 versicolor59           6.6         2.9          4.6         1.3 versicolor60           5.2         2.7          3.9         1.4 versicolor61           5.0         2.0          3.5         1.0 versicolor62           5.9         3.0          4.2         1.5 versicolor63           6.0         2.2          4.0         1.0 versicolor64           6.1         2.9          4.7         1.4 versicolor65           5.6         2.9          3.6         1.3 versicolor66           6.7         3.1          4.4         1.4 versicolor67           5.6         3.0          4.5         1.5 versicolor68           5.8         2.7          4.1         1.0 versicolor69           6.2         2.2          4.5         1.5 versicolor70           5.6         2.5          3.9         1.1 versicolor71           5.9         3.2          4.8         1.8 versicolor72           6.1         2.8          4.0         1.3 versicolor73           6.3         2.5          4.9         1.5 versicolor74           6.1         2.8          4.7         1.2 versicolor75           6.4         2.9          4.3         1.3 versicolor76           6.6         3.0          4.4         1.4 versicolor77           6.8         2.8          4.8         1.4 versicolor78           6.7         3.0          5.0         1.7 versicolor79           6.0         2.9          4.5         1.5 versicolor80           5.7         2.6          3.5         1.0 versicolor81           5.5         2.4          3.8         1.1 versicolor82           5.5         2.4          3.7         1.0 versicolor83           5.8         2.7          3.9         1.2 versicolor84           6.0         2.7          5.1         1.6 versicolor85           5.4         3.0          4.5         1.5 versicolor86           6.0         3.4          4.5         1.6 versicolor87           6.7         3.1          4.7         1.5 versicolor88           6.3         2.3          4.4         1.3 versicolor89           5.6         3.0          4.1         1.3 versicolor90           5.5         2.5          4.0         1.3 versicolor91           5.5         2.6          4.4         1.2 versicolor92           6.1         3.0          4.6         1.4 versicolor93           5.8         2.6          4.0         1.2 versicolor94           5.0         2.3          3.3         1.0 versicolor95           5.6         2.7          4.2         1.3 versicolor96           5.7         3.0          4.2         1.2 versicolor97           5.7         2.9          4.2         1.3 versicolor98           6.2         2.9          4.3         1.3 versicolor99           5.1         2.5          3.0         1.1 versicolor100          5.7         2.8          4.1         1.3 versicolor101          6.3         3.3          6.0         2.5  virginica102          5.8         2.7          5.1         1.9  virginica103          7.1         3.0          5.9         2.1  virginica104          6.3         2.9          5.6         1.8  virginica105          6.5         3.0          5.8         2.2  virginica106          7.6         3.0          6.6         2.1  virginica107          4.9         2.5          4.5         1.7  virginica108          7.3         2.9          6.3         1.8  virginica109          6.7         2.5          5.8         1.8  virginica110          7.2         3.6          6.1         2.5  virginica111          6.5         3.2          5.1         2.0  virginica112          6.4         2.7          5.3         1.9  virginica113          6.8         3.0          5.5         2.1  virginica114          5.7         2.5          5.0         2.0  virginica115          5.8         2.8          5.1         2.4  virginica116          6.4         3.2          5.3         2.3  virginica117          6.5         3.0          5.5         1.8  virginica118          7.7         3.8          6.7         2.2  virginica119          7.7         2.6          6.9         2.3  virginica120          6.0         2.2          5.0         1.5  virginica121          6.9         3.2          5.7         2.3  virginica122          5.6         2.8          4.9         2.0  virginica123          7.7         2.8          6.7         2.0  virginica124          6.3         2.7          4.9         1.8  virginica125          6.7         3.3          5.7         2.1  virginica126          7.2         3.2          6.0         1.8  virginica127          6.2         2.8          4.8         1.8  virginica128          6.1         3.0          4.9         1.8  virginica129          6.4         2.8          5.6         2.1  virginica130          7.2         3.0          5.8         1.6  virginica131          7.4         2.8          6.1         1.9  virginica132          7.9         3.8          6.4         2.0  virginica133          6.4         2.8          5.6         2.2  virginica134          6.3         2.8          5.1         1.5  virginica135          6.1         2.6          5.6         1.4  virginica136          7.7         3.0          6.1         2.3  virginica137          6.3         3.4          5.6         2.4  virginica138          6.4         3.1          5.5         1.8  virginica139          6.0         3.0          4.8         1.8  virginica140          6.9         3.1          5.4         2.1  virginica141          6.7         3.1          5.6         2.4  virginica142          6.9         3.1          5.1         2.3  virginica143          5.8         2.7          5.1         1.9  virginica144          6.8         3.2          5.9         2.3  virginica145          6.7         3.3          5.7         2.5  virginica146          6.7         3.0          5.2         2.3  virginica147          6.3         2.5          5.0         1.9  virginica148          6.5         3.0          5.2         2.0  virginica149          6.2         3.4          5.4         2.3  virginica150          5.9         3.0          5.1         1.8  virginica

Sepal意思是“花萼 ”，Petal意思是“ 花瓣”。很明显，前四列是花萼和花瓣的特征，第五列代表相应的分类。我们可以用这个数据集进行贝叶斯训练。

先看一下，对这个数据集summary的结果：

> 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

训练并查看训练结果：

> classifier<-naiveBayes(iris[,1:4], iris[,5]) > classifierNaive Bayes Classifier for Discrete PredictorsCall: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.Lengthiris[, 5]     [,1]      [,2]  setosa     5.006 0.3524897  versicolor 5.936 0.5161711  virginica  6.588 0.6358796            Sepal.Widthiris[, 5]     [,1]      [,2]  setosa     3.428 0.3790644  versicolor 2.770 0.3137983  virginica  2.974 0.3224966            Petal.Lengthiris[, 5]     [,1]      [,2]  setosa     1.462 0.1736640  versicolor 4.260 0.4699110  virginica  5.552 0.5518947            Petal.Widthiris[, 5]     [,1]      [,2]  setosa     0.246 0.1053856  versicolor 1.326 0.1977527  virginica  2.026 0.2746501> classifier$aprioriiris[, 5]    setosa versicolor  virginica         50         50         50 > classifier$tables$Sepal.Length            Sepal.Lengthiris[, 5]     [,1]      [,2]  setosa     5.006 0.3524897  versicolor 5.936 0.5161711  virginica  6.588 0.6358796$Sepal.Width            Sepal.Widthiris[, 5]     [,1]      [,2]  setosa     3.428 0.3790644  versicolor 2.770 0.3137983  virginica  2.974 0.3224966$Petal.Length            Petal.Lengthiris[, 5]     [,1]      [,2]  setosa     1.462 0.1736640  versicolor 4.260 0.4699110  virginica  5.552 0.5518947$Petal.Width            Petal.Widthiris[, 5]     [,1]      [,2]  setosa     0.246 0.1053856  versicolor 1.326 0.1977527  virginica  2.026 0.2746501

classifier中：

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

很好理解，就是类别的先验概率。
而：

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

是特征Petal.Width的条件概率，在这个贝叶斯实现中，特征是数值型数据（而且还还有小数部分），这里假设概率密度符合高斯分布。比如对于特征Petal.Width，其属于setosa的概率符合mean为0.246，标准方差为0.1053856的高斯分布。



预测：
预测iris数据集中的第一个数据：

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

iris[1,-5]表示第一行的前4列。

看一下该分类器的效果：

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

分类效果还是不错的。

自己构造一个新的数据并预测：

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

如果少一个特征（只有三个特征）：

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

下面看一下，这个库如何处理标称型特征：

数据如下：

> 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 repairs1     H     B       Y2     H     B       N3     H     N       Y4     H     N       N5     T     B       Y6     T     B       N7     T     N       Y8     T     N       N

贝叶斯之：

> classifier<-naiveBayes(dataset[,1:2], dataset[,3]) > classifierNaive Bayes Classifier for Discrete PredictorsCall:naiveBayes.default(x = dataset[, 1:2], y = dataset[, 3])A-priori probabilities:dataset[, 3]  N   Y 0.5 0.5 Conditional probabilities:            modeldataset[, 3]   H   T           N 0.5 0.5           Y 0.5 0.5            placedataset[, 3]   B   N           N 0.5 0.5           Y 0.5 0.5

好了，预测一下：

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

perfect！

补充一下，如果某个数据缺少某些特征：

可以用NA代替该特征：

> 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 repairs1     H     B       Y2     H     B       N3     H     N       Y4     H     N       N5     T     B       Y6     T     B       N7     T  <NA>       Y8     T  <NA>       N> classifier<-naiveBayes(dataset[,1:2], dataset[,3]) > classifierNaive Bayes Classifier for Discrete PredictorsCall:naiveBayes.default(x = dataset[, 1:2], y = dataset[, 3])A-priori probabilities:dataset[, 3]  N   Y 0.5 0.5 Conditional probabilities:            modeldataset[, 3]   H   T           N 0.5 0.5           Y 0.5 0.5            placedataset[, 3]         B         N           N 0.6666667 0.3333333           Y 0.6666667 0.3333333

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