r - 在 R 中循环一个函数
问题描述
我在 R 中编写了一个交叉验证/网格搜索样式代码,它试图找到给定值的最佳阈值mtry
(使用随机森林算法)。我已经使用Sonar
库中的数据在下面发布了我的代码mlbench
但是,此代码似乎存在一些问题。
library(caret)
library(mlbench)
library(randomForest)
res <- matrix(0, nrow = 10, ncol = 6)
colnames(res) <- c("mtry","Threshhold","Accuracy", "PositivePred", "NegativePred", "F-value")
out <- matrix(0, nrow = 17, ncol = 6)
colnames(out) <- c("mtry","Threshhold","Avg.Accuracy", "Avg.PosPred", "Avg.NegPred", "Avg.F_Value")
rep <- matrix(0, nrow = 10, ncol = 6)
colnames(out) <- c("mtry","Threshhold","Avg_Accuracy", "Avg_PosPred", "Avg_NegPred", "Avg_F_Value")
data(Sonar)
N=Sonar
### creating 10 folds
folds <- cut(seq(1,nrow(N)),breaks=10,labels=FALSE)
for (mtry in 5:14) {
K=mtry-4
for(thresh in seq(1,9,0.5)) {
J = 2*thresh-1
dataset<-N[sample(nrow(N)),] #### mix up the dataset N
for(I in 1:10){
#Segement your data by fold using the which() function
testIndexes <- which(folds==I,arr.ind=TRUE)
N_test <- dataset[testIndexes, ] ### select each fold for test
N_train <- dataset[-testIndexes, ] ### select rest for training
rf = randomForest(Class~., data = N_train, mtry=mtry, ntree=500)
pred = predict(rf, N_test, type="prob")
label = as.factor(ifelse(pred[,2]>=thresh,"M","R"))
confusion = confusionMatrix(N_test$Class, label)
res[I,1]=mtry
res[I,2]=thresh
res[I,3]=confusion$overall[1]
res[I,4]=confusion$byClass[3]
res[I,5]=confusion$byClass[4]
res[I,6]=confusion$byClass[7]
}
print(res)
out[J,1] = mtry
out[J,2] = thresh
out[J,3] = mean(res[,2])
out[J,4] = mean(res[,3])
out[J,5] = mean(res[,4])
out[J,6] = mean(res[,5])
}
print(out)
rep[K,1] = mtry
rep[K,2] = thresh
rep[K,3] = mean(out[,2])
rep[K,4] = mean(out[,3])
rep[K,5] = mean(out[,4])
rep[K,6] = mean(out[,5])
}
print(rep)
早些时候,我用“iris”数据集写了一个类似的代码,我似乎没有任何问题:
library(caret)
library(randomForest)
data(iris)
N <- iris
N$Species = ifelse(N$Species == "setosa", "a", "b")
N$Species = as.factor(N$Species)
res <- matrix(0, nrow = 10, ncol = 5)
colnames(res) <- c("Threshhold","Accuracy", "PositivePred", "NegativePred", "F-value")
out <- matrix(0, nrow = 9, ncol = 5)
colnames(out) <- c("Threshhold","Avg.Accuracy", "Avg.PosPred", "Avg.NegPred", "Avg.F_Value")
### creating 10 folds
folds <- cut(seq(1,nrow(N)),breaks=10,labels=FALSE)
for(J in 1:9) {
thresh = J/10
dataset<-N[sample(nrow(N)),] #### mix up the dataset N
for(I in 1:10){
#Segement your data by fold using the which() function
testIndexes <- which(folds==I,arr.ind=TRUE)
N_test <- dataset[testIndexes, ] ### select each fold for test
N_train <- dataset[-testIndexes, ] ### select rest for training
rf = randomForest(Species~., data = N_train, mtry=3, ntree=10)
pred = predict(rf, N_test, type="prob")
label = as.factor(ifelse(pred[,1]>=thresh,"a","b"))
confusion = confusionMatrix(N_test$Species, label)
res[I,1]=thresh
res[I,2]=confusion$overall[1]
res[I,3]=confusion$byClass[3]
res[I,4]=confusion$byClass[4]
res[I,5]=confusion$byClass[7]
}
print(res)
out[J,1] = thresh
out[J,2] = mean(res[,2])
out[J,3] = mean(res[,3])
out[J,4] = mean(res[,4])
out[J,5] = mean(res[,5])
}
print(out)
有人可以帮我调试第一个代码吗?谢谢
解决方案
您需要)
在 for 循环中关闭括号。
替换这个
for(thresh in seq(1,9,0.5) {
和
for(thresh in seq(1,9,0.5)) {
更新:
此外,您似乎thresh
总是高于 1 在标签中给出单个值R
,因为它永远不会高于thresh
。
label = as.factor(ifelse(pred[,2]>=thresh,"M","R"))
这在下一个语句中产生了一个问题
confusion = confusionMatrix(N_test$Class, label)
我用 0.5 进行了测试,我没有收到任何错误。
label = as.factor(ifelse(pred[,2]>=0.5,"M","R"))
如果你能定义一个更好的thresh
- 保持在 0 和 1 之间,你应该没问题。
推荐阅读
- java - 解决“多个 Jar 文件”又名“从多个位置扫描”
- android - viewpager2 与片段,其中 viewpager2 在销毁第一个父亲片段后也没有被销毁
- jquery - 如何使用 jquery 将“2020-10-08 09:38:08”转换为 2020 年 3 月格式
- simple-injector - 使用 Lifestyle.Singleton 时注册的 COM 接口无法解析
- algorithm - 使用重心将任何图形转换为平面的复杂性
- perl - 如何做 Perl 状态机(FSM)来解析比特流(字节序列)?
- flutter - 使用异步更新提供程序后,具有未来构建器的小部件不会删除小部件
- azure - 如何在 Azure 云计费导出中查找 ServiceName
- javascript - The "listener" argument must be of type function. Received undefined
- r - 在ggplot中设置ylims时如何删除小数位?