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数据分析与挖掘 - R语言:贝叶斯分类算法(案例三)

时间:2016-05-25 14:59:32      阅读:434      评论:0      收藏:0      [点我收藏+]

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案例三比较简单,不需要自己写公式算法,使用了R自带的naiveBayes函数。

 

代码如下:

> library(e1071)
> classifier<-naiveBayes(iris[,1:4], iris[,5]) #或写成下面形式,都可以。 > classifier<- naiveBayes(Species ~ ., data = iris) #其中Species是类别变量 #预测 > predict(classifier, iris[1, -5])

预测结果为:

[1] setosa
Levels: setosa versicolor virginica

和原数据一样!

 

*********************************这里是分割线**************************************

我们再拿这个方法来预测一下案例一中的样本。

#样本数据集:
mydata <- matrix(c("sunny","hot","high","weak","no",  
                 "sunny","hot","high","strong","no",  
                 "overcast","hot","high","weak","yes",  
                 "rain","mild","high","weak","yes",  
                 "rain","cool","normal","weak","yes",  
                 "rain","cool","normal","strong","no",  
                 "overcast","cool","normal","strong","yes",  
                 "sunny","mild","high","weak","no",  
                 "sunny","cool","normal","weak","yes",  
                 "rain","mild","normal","weak","yes",  
                 "sunny","mild","normal","strong","yes",  
                 "overcast","mild","high","strong","yes",  
                 "overcast","hot","normal","weak","yes",  
                 "rain","mild","high","strong","no"), byrow = TRUE, nrow=14, ncol=5)

#添加列名:
colnames(mydata) <-  c("outlook","temperature","humidity","wind","playtennis")

#贝叶斯算法:
m<-naiveBayes(mydata[,1:4], mydata[,5]) 
#或使用下面的方法
m<- naiveBayes(playtennis ~ ., data = mydata)    
#报错:Error in sum(x) : invalid ‘type‘ (character) of argument 无效的类型,只能是数字? #创建预测数据集: new_data = data.frame(outlook="rain", temperature="cool", humidity="normal", wind="strong", playtennis="so") #预测: predict(m, new_data)

在使用naiveBayes函数时报错:Error in sum(x) : invalid ‘type‘ (character) of argument

我们看一下官方文档,对data有这样一句描述:

data  Either a data frame of predictors (categorical and/or numeric) or a contingency table.

data是一个数字类型的数据框。

 

数据分析与挖掘 - R语言:贝叶斯分类算法(案例三)

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原文地址:http://www.cnblogs.com/hunttown/p/5526786.html

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