详解R语言caret包trainControl函数

第详解R语言caret包trainControl函数目录trainControl参数详解源码参数详解示例

trainControl参数详解

源码

caret::trainControl-

function(method="boot",number=ifelse(grepl("cv",method),10,25),repeats=ifelse(grepl("[d_]cv$",method),1,NA),p=0.75,search="grid",initialWindow=NULL,horizon=1,fixedWindow=TRUE,skip=0,verboseIter=FALSE，returnData=TRUE,returnResamp="final",savePredictions=FALSE,

classProbs=FALSE,summaryFunction=defaultSummary,selectionFunction="best",

preProcOptions=list(thresh=0.95,ICAcomp=3,k=5,

freqCut=95/5,uniqueCut=10,cutoff=0.9),sampling=NULL,

index=NULL,indexOut=NULL,indexFinal=NULL,timingSamps=0,

predictionBounds=rep(FALSE,2),seeds=NA,adaptive=list(min=5,

alpha=0.05,method="gls",complete=TRUE),

trim=FALSE,allowParallel=TRUE)

if(is.null(selectionFunction))

stop("nullselectionFunctionvaluesnotallowed")

if(!(returnResamp%in%c("all","final","none")))

stop("incorrectvalueofreturnResamp")

if(length(predictionBounds)0length(predictionBounds)!=

stop("'predictionBounds'shouldbealogicalornumericvectoroflength2")

if(any(names(preProcOptions)=="method"))

stop("'method'cannotbespecifiedhere")

if(any(names(preProcOptions)=="x"))

stop("'x'cannotbespecifiedhere")

if(!is.na(repeats)!(method%in%c("repeatedcv",

"adaptive_cv")))

warning("`repeats`hasnomeaningforthisresamplingmethod.",

call.=FALSE)

if(!(adaptive$method%in%c("gls","BT")))

stop("incorrectvalueofadaptive$method")

if(adaptive$alpha1e-07|adaptive$alpha1)

stop("incorrectvalueofadaptive$alpha")

if(grepl("adapt",method)){

num-if(method=="adaptive_cv")

number*repeats

elsenumber

if(adaptive$min=num)

stop(paste("adaptive$minshouldbelessthan",

num))

if(adaptive$min=1)

stop("adaptive$minshouldbegreaterthan1")

if(!(search%in%c("grid","random")))

stop("`search`shouldbeeither'grid'or'random'")

if(method=="oob"any(names(match.call())=="summaryFunction")){

warning("Customsummarymeasurescannotbecomputedforout-of-bagresampling.",

"Thisvalueof`summaryFunction`willbeignored.",

call.=FALSE)

list(method=method,number=number,repeats=repeats,

search=search,p=p,initialWindow=initialWindow,

horizon=horizon,fixedWindow=fixedWindow,skip=skip,

verboseIter=verboseIter,returnData=returnData,returnResamp=returnResamp,

savePredictions=savePredictions,classProbs=classProbs,

summaryFunction=summaryFunction,selectionFunction=selectionFunction,

preProcOptions=preProcOptions,sampling=sampling,

index=index,indexOut=indexOut,indexFinal=indexFinal,

timingSamps=timingSamps,predictionBounds=predictionBounds,

seeds=seeds,adaptive=adaptive,trim=trim,allowParallel=allowParallel)

参数详解

trainControl所有参数详解method重抽样方法:Bootstrap(有放回随机抽样)、Bootstrap632(有放回随机抽样扩展)、LOOCV(留一交叉验证)、LGOCV(蒙特卡罗交叉验证)、cv(k折交叉验证)、repeatedcv(重复的k折交叉验证)、optimism_boot(Efron,B.,Tibshirani,R.J.(1994).Anintroductiontothebootstrap,pages249-252.CRCpress.)、none(仅使用一个训练集拟合模型)、oob(袋外估计：随机森林、多元自适应回归样条、树模型、灵活判别分析、条件树)number控制K折交叉验证的数目或者Bootstrap和LGOCV的抽样迭代次数repeats控制重复交叉验证的次数pLGOCV：控制训练比例verboseIter输出训练日志的逻辑变量returnData逻辑变量，把数据保存到trainingData中（str(trainControl)查看）searchsearch=grid(网格搜索)，random(随机搜索)returnResamp包含以下值的字符串：final、all、none，设定有多少抽样性能度量被保存。classProbs是否计算类别概率summaryFunction根据重抽样计算模型性能的函数selectionFunction选择最优参数的函数index指定重抽样样本(使用相同的重抽样样本评估不同的算法、模型)allowParallel是否允许并行

示例

library(mlbench)#使用包中的数据

Warningmessage:

程辑包‘mlbench'是用R版本4.1.3来建造的

data(Sonar)

str(Sonar[,1:10])

'data.frame':208obs.of10variables:

$V1:num0.020.04530.02620.010.07620.02860.03170.05190.02230.0164...

$V2:num0.03710.05230.05820.01710.06660.04530.09560.05480.03750.0173...

$V3:num0.04280.08430.10990.06230.0481...

$V4:num0.02070.06890.10830.02050.0394...

$V5:num0.09540.11830.09740.02050.059...

$V6:num0.09860.25830.2280.03680.0649...

$V7:num0.1540.2160.2430.110.121...

$V8:num0.160.3480.3770.1280.247...

$V9:num0.31090.33370.55980.05980.3564...

$V10:num0.2110.2870.6190.1260.446...

数据分割：

library(caret)

set.seed(998)

inTraining-createDataPartition(Sonar$Class,p=.75,list=FALSE)

training-Sonar[inTraining,]#训练集

testing-Sonar[-inTraining,]#测试集

模型拟合：

fitControl-trainControl(##10折交叉验证

method="repeatedcv",

number=10,

##重复10次

repeats=1)

set.seed(825)

gbmFit1-train(Class~.,data=training,

method="gbm",#助推树

trControl=fitControl,

verbose=FALSE)

gbmFit1

StochasticGradientBoosting

157samples

60predictor

2classes:'M','R'

Nopre-processing

Resampling:Cross-Validated(10fold,repeated10times)

Summaryofsamplesizes:141,142,141,142,141,142,...

Resamplingresultsacrosstuningparameters:

interaction.depthn.treesAccuracyKappa

1500.79357840.5797839

11000.81710780.6290208

11500.82196080.6383173

2500.80419120.6027771

21000.82961760.6544713

21500.82836270.6520251

3500.81103430.6170317

31000.83012750.6551379

31500.83103430.6577252

Tuningparameter'shrinkage'washeld