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简介
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模拟数据
set.seed(888)
df1 <- data.frame(x1 = runif(200,0,100),x2 = runif(200,0,100))
df1 <- transform(df1,y = 1+ifelse(100-x1-x2+rnorm(200,sd = 10) < 0,0,
ifelse(100-2*x2+rnorm(200,sd=10)<2,1,2)))
df1$y <- as.factor(df1$y)
df1$tag <- c(rep("train",150),rep("test",50))
str(df1)
# 'data.frame': 200 obs. of 4 variables:
# $ x1 : num 2.55 34.67 6.12 68.38 76.73 ...
# $ x2 : num 76.73 88.59 19.9 8.54 11.36 ...
# $ y : Factor w/ 3 levels "1","2","3": 2 1 3 3 3 3 1 3 3 1 ...
# $ tag: chr "train" "train" "train" "train" ...
- 查看模拟的数据
library(ggplot2)
qplot(x1,x2,data = df1,colour =y,shape =tag)
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- 整理数据并训练模型
library(class)
train <- df1[1:150,1:2]
train.label <- df1[1:150,3]
test <- df1[151:200,1:2]
test.label <- df1[151:200,3]
pred <- knn(train = train,test = test,cl = train.label,k =6)
pred #返回test数据集里面观察对象的预测
# [1] 3 1 1 3 1 3 2 1 1 3 3 3 2 1 3 1 1 2 3 1 1 1 1 1 1 2 1 1 1 1 1 1 1 3 1 1 2 3 3
# [40] 1 1 1 2 1 2 3 1 1 1 1
# Levels: 1 2 3
- 对拟合结果的评估观察
#install.packages("gmodels")
library(gmodels)
CrossTable(x = test.label,y = pred,prop.chisq = FALSE)
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- 拟合准确性的评估
table <- CrossTable(x = test.label,y = pred,prop.chisq = TRUE)
tp1 <- table$t[1,1]
tp2 <- table$t[2,2]
tp3 <- table$t[3,3]
tn1 <- table$t[2,2]+table$t[2,3]+table$t[3,2]+table$t[3,3]
tn2 <- table$t[1,1]+table$t[1,3]+table$t[3,1]+table$t[3,3]
tn3 <- table$t[1,1]+table$t[1,2]+table$t[2,1]+table$t[2,2]
fn1 <- table$t[1,2]+table$t[1,3]
fn2 <- table$t[2,1]+table$t[2,3]
fn3 <- table$t[3,1]+table$t[3,2]
fp1 <- table$t[2,1]+table$t[3,1]
fp2 <- table$t[1,2]+table$t[3,2]
fp3 <- table$t[1,3]+table$t[2,3]
accuracy <- (((tp1+tn1)/(tp1+fn1+fp1+tn1))+((tp2+tn2)/(tp2+fn2+fp2+tn2))+((tp3+tn3)/(tp3+fn3+fp3+tn3)))/3
accuracy
#[1] 0.9333333
- 敏感性和特异性评估
sen1 <- tp1/(tp1+fn1)
sp1 <- tn1/(tn1+fp1)
sen1
# [1] 1
sp1
#[1] 0.9047619
- Multiclass area under the curve (AUC)
library(pROC)
multiclass.roc(response = test.label,predictor = as.ordered(pred))
# Call:
# multiclass.roc.default(response = test.label, predictor = as.ordered(pred))
#
# Data: as.ordered(pred) with 3 levels of test.label: 1, 2, 3.
# Multi-class area under the curve: 0.9212
- Kappa statistic
手动计算
table <- table(test.label,pred)
table
# pred
# test.label 1 2 3
# 1 29 0 0
# 2 2 6 2
# 3 0 1 10
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自动计算kappa statitic
#install.packages("psych")
library(psych)
cohen.kappa(x=cbind(test.label,pred)) # 取unweighted kappa
# Call: cohen.kappa1(x = x, w = w, n.obs = n.obs, alpha = alpha, levels = levels)
#
# Cohen Kappa and Weighted Kappa correlation coefficients and confidence boundaries
# lower estimate upper
# unweighted kappa 0.68 0.82 0.96
# weighted kappa 0.93 0.93 0.93
#
# Number of subjects = 50
- 调整k值对knn模型预测准确性的影响
accuracyCal <- function(N){
accuracy <- 1
for (x in 1:N){
pred <- knn(train = train,test = test,cl = train.label,k =x)
table <- table(test.label,pred)
tp1 <- table[1,1]
tp2 <- table[2,2]
tp3 <- table[3,3]
tn1 <- table[2,2]+table[2,3]+table[3,2]+table[3,3]
tn2 <- table[1,1]+table[1,3]+table[3,1]+table[3,3]
tn3 <- table[1,1]+table[1,2]+table[2,1]+table[2,2]
fn1 <- table[1,2]+table[1,3]
fn2 <- table[2,1]+table[2,3]
fn3 <- table[3,1]+table[3,2]
fp1 <- table[2,1]+table[3,1]
fp2 <- table[1,2]+table[3,2]
fp3 <- table[1,3]+table[2,3]
accuracy <- c(accuracy,(((tp1+tn1)/(tp1+fn1+fp1+tn1))+
((tp2+tn2)/(tp2+fn2+fp2+tn2))+
((tp3+tn3)/(tp3+fn3+fp3+tn3)))/3)
}
return(accuracy[-1])
}
# install.packages("TeachingDemos")
library(TeachingDemos)
qplot(seq(1:150),accuracyCal(150),xlab = "k values",
ylab = "Average accuracy",geom = c("point","smooth"))
subplot(plot(seq(1:30),accuracyCal(30),col=2,xlab = "",ylab = "",cex.axis = 0.8),
x = grconvertX(c(0,0.75),from = "npc"),
y = grconvertY(c(0,0.45),from = "npc"),
type = "fig",pars = list(mar=c(0,0,1.5,1.5)+0.1))
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参考资料
章仲恒教授丁香园课程：K-近邻取样
Zhang Zhongheng. Introduction to machine learning: k-nearest neighbors
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