iris数据集
iris以鸢尾花的特征作为数据来源，常用在分类操作中。该数据集由3种不同类型的鸢尾花的50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的，后两个种类是非线性可分离的。
library(ggplot2) summary(iris) qplot(Petal.Length, Petal.Width, data=iris, color=Species)- 1
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1：C5.0决策树
先加载所需要的包
library(C50) library(printr)- 1
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对iris数据集进行抽样，获得训练样本和测试样本
train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]- 1
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利用C5.0函数对训练样本进行模型训练
model <- C5.0(Species ~ ., data = iris.train)- 1
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对测试样本进行预测
results <- predict(object = model, newdata = iris.test, type = "class")
confusion_matrix=table(results, iris.test$Species)
confusion_matrix- 1
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计算错误率
error=1-sum(diag(confusion_matrix))/nrow(iris.test)- 1
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预测错误率为0.12
2：K-means
模型建立
library(stats)
library(printr)
model <- kmeans(x = subset(iris, select = -Species), centers = 3)- 1
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分类性能测试
table(model$cluster, iris$Species)
/ setosa versicolor virginica
1 33 0 0
2 17 4 0
3 0 46 50- 1
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3：Support Vector Machines
导入包
library(e1071)
library(printr)
对iris数据集进行抽样，获得训练样本和测试样本
train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]- 1
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利用C5.0函数对训练样本进行模型训练
model <- svm(Species ~ ., data = iris.train)
对测试样本进行预测
results <- predict(object = model, newdata = iris.test, type = "class")
confusion_matrix=table(results, iris.test$Species)
confusion_matrix
results/ setosa versicolor virginica
setosa 12 0 0
versicolor 0 19 0
virginica 0 1 18
计算错误率
error=1-sum(diag(confusion_matrix))/nrow(iris.test)
预测错误率为0.02- 1
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4：Apriori
导入包和数据集
library(arules)
library(printr)
data("Adult")
训练模型
rules <- apriori(Adult,
parameter = list(support = 0.4, confidence = 0.7),
appearance = list(rhs = c("race=White", "sex=Male"), default = "lhs"))
获得前五的关联关系
rules.sorted <- sort(rules, by = "lift")
top5.rules <- head(rules.sorted, 5)
as(top5.rules, "data.frame")
rules support confidence lift
2 {relationship=Husband} => {sex=Male} 0.4036485 0.9999493 1.495851
12 {marital-status=Married-civ-spouse,relationship=Husband} => {sex=Male} 0.4034028 0.9999492 1.495851
3 {marital-status=Married-civ-spouse} => {sex=Male} 0.4074157 0.8891818 1.330151
4 {marital-status=Married-civ-spouse} => {race=White} 0.4105892 0.8961080 1.048027
19 {workclass=Private,native-country=United-States} => {race=White} 0.5433848 0.8804113 1.029669- 1
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5:EM算法
library(mclust) library(printr) model <- Mclust(subset(iris, select = -Species)) table(model$classification, iris$Species) / setosa versicolor virginica 1 50 0 0 2 0 50 50 - 1
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6：PageRank
PageRank用来计算图中各点的相关程度，其原理是马尔科夫链
library(igraph) library(dplyr) library(printr) 生成随机的网络图 g <- random.graph.game(n = 10, p.or.m = 1/4, directed = TRUE) plot(g) 对每个节点计算rankpage值 pr <- page.rank(g)$vector df <- data.frame(Object = 1:10, PageRank = pr) arrange(df, desc(PageRank)) Object PageRank 10 0.1768655 7 0.1369388 1 0.1263876 4 0.1198167 2 0.1161824 9 0.0891266 6 0.0847579 8 0.0793286 5 0.0390147 3 0.0315813- 1
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7:adaboost
library(adabag)
library(printr)
train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]
模型训练
model <- boosting(Species ~ ., data = iris.train)
训练结果
results <- predict(object = model, newdata = iris.test, type = "class")
results$confusion
Predicted Class/Observed Class setosa versicolor virginica
setosa 15 0 0
versicolor 0 18 4
virginica 0 0 13- 1
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8:kNN
library(class)
library(printr)
train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]
模型训练
results <- knn(train = subset(iris.train, select = -Species),
test = subset(iris.test, select = -Species),
cl = iris.train$Species)
分类效果
table(results, iris.test$Species)
results/ setosa versicolor virginica
setosa 22 0 0
versicolor 0 10 0
virginica 0 1 17- 1
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9:naive bayes
library(e1071)
library(printr)
train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]
训练集训练模型
model <- naiveBayes(x = subset(iris.train, select=-Species), y = iris.train$Species)
测试集预测效果
results <- predict(object = model, newdata = iris.test,type ="class")
table(results, iris.test$Species)
results/ setosa versicolor virginica
setosa 18 0 0
versicolor 0 17 0
virginica 0 4 11- 1
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10:cart
library(rpart)
library(printr)
train.indeces <- sample(1:nrow(iris), 100)
iris.train <- iris[train.indeces, ]
iris.test <- iris[-train.indeces, ]
训练模型
model <- rpart(Species ~ ., data = iris.train)
测试模型
results <- predict(object = model, newdata = iris.test, type = "class")
table(results, iris.test$Species)
results/ setosa versicolor virginica
setosa 15 0 0
versicolor 0 16 6
virginica 0 1 12
转自：http://blog.csdn.net/cmddds11235/article/details/47724871
