决策树的“train”函数如何选择最佳曲调？

Question

为了估计所谓的复杂度参数cp（修剪树很方便），我使用了网格搜索和另一个包caret的函数。这是代码：

dataset = dataset[3:5]

dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1))

library(caTools)
set.seed(123)
split = sample.split(dataset$Purchased, SplitRatio = 0.75)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

training_set[-3] = scale(training_set[-3])
test_set[-3] = scale(test_set[-3])


library(rpart)
library(caret)
youdenSumary <- function(data, lev = NULL, model = NULL){
  if (length(lev) > 2) {
    stop(paste("Your outcome has", length(lev), "levels. The joudenSumary() function isn't appropriate."))
  }
  if (!all(levels(data[, "pred"]) == lev)) {
    stop("levels of observed and predicted data do not match")
  }
  Sens <- caret::sensitivity(data[, "pred"], data[, "obs"], lev[1]) 
  Spec <- caret::specificity(data[, "pred"], data[, "obs"], lev[2])
  j <- (Sens + Spec)/2
  out <- c(j, Spec, Sens)
  names(out) <- c("j", "Spec", "Sens")
  out
}


library(caret)
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 20,
                       search = "grid",summaryFunction = youdenSumary)


classifier = train(form = Purchased ~ ., data = training_set, method = 'rpart',
                   trControl=trctrl,tuneLength = 10,metric = "j")
classifier

此代码产生以下结果：

cp          j          Spec       Sens     
0.00000000  0.9133409  0.9141818  0.9125000
0.06126687  0.9205592  0.9345000  0.9066184
0.12253375  0.9205592  0.9345000  0.9066184
0.18380062  0.9205592  0.9345000  0.9066184
0.24506750  0.8009091  0.6773182  0.9245000
0.30633437  0.7884659  0.6396818  0.9372500
0.36760125  0.7884659  0.6396818  0.9372500
0.42886812  0.7884659  0.6396818  0.9372500
0.49013499  0.7884659  0.6396818  0.9372500
0.55140187  0.6588846  0.3660455  0.9517237

j was used to select the optimal model using the largest value.
The final value used for the model was cp = 0.1838006.

我想知道算法如何为 cp 选择候选值。我阅读了 cp 的 10 个可能值；该算法在其中选择最大化 j 的值，这对我来说很清楚。但是要分配给 cp 的可能值从何而来？

数据集如下：

> dput(dataset)
structure(list(Age = c(19L, 35L, 26L, 27L, 19L, 27L, 27L, 32L, 
25L, 35L, 26L, 26L, 20L, 32L, 18L, 29L, 47L, 45L, 46L, 48L, 45L, 
47L, 48L, 45L, 46L, 47L, 49L, 47L, 29L, 31L, 31L, 27L, 21L, 28L, 
27L, 35L, 33L, 30L, 26L, 27L, 27L, 33L, 35L, 30L, 28L, 23L, 25L, 
27L, 30L, 31L, 24L, 18L, 29L, 35L, 27L, 24L, 23L, 28L, 22L, 32L, 
27L, 25L, 23L, 32L, 59L, 24L, 24L, 23L, 22L, 31L, 25L, 24L, 20L, 
33L, 32L, 34L, 18L, 22L, 28L, 26L, 30L, 39L, 20L, 35L, 30L, 31L, 
24L, 28L, 26L, 35L, 22L, 30L, 26L, 29L, 29L, 35L, 35L, 28L, 35L, 
28L, 27L, 28L, 32L, 33L, 19L, 21L, 26L, 27L, 26L, 38L, 39L, 37L, 
38L, 37L, 42L, 40L, 35L, 36L, 40L, 41L, 36L, 37L, 40L, 35L, 41L, 
39L, 42L, 26L, 30L, 26L, 31L, 33L, 30L, 21L, 28L, 23L, 20L, 30L, 
28L, 19L, 19L, 18L, 35L, 30L, 34L, 24L, 27L, 41L, 29L, 20L, 26L, 
41L, 31L, 36L, 40L, 31L, 46L, 29L, 26L, 32L, 32L, 25L, 37L, 35L, 
33L, 18L, 22L, 35L, 29L, 29L, 21L, 34L, 26L, 34L, 34L, 23L, 35L, 
25L, 24L, 31L, 26L, 31L, 32L, 33L, 33L, 31L, 20L, 33L, 35L, 28L, 
24L, 19L, 29L, 19L, 28L, 34L, 30L, 20L, 26L, 35L, 35L, 49L, 39L, 
41L, 58L, 47L, 55L, 52L, 40L, 46L, 48L, 52L, 59L, 35L, 47L, 60L, 
49L, 40L, 46L, 59L, 41L, 35L, 37L, 60L, 35L, 37L, 36L, 56L, 40L, 
42L, 35L, 39L, 40L, 49L, 38L, 46L, 40L, 37L, 46L, 53L, 42L, 38L, 
50L, 56L, 41L, 51L, 35L, 57L, 41L, 35L, 44L, 37L, 48L, 37L, 50L, 
52L, 41L, 40L, 58L, 45L, 35L, 36L, 55L, 35L, 48L, 42L, 40L, 37L, 
47L, 40L, 43L, 59L, 60L, 39L, 57L, 57L, 38L, 49L, 52L, 50L, 59L, 
35L, 37L, 52L, 48L, 37L, 37L, 48L, 41L, 37L, 39L, 49L, 55L, 37L, 
35L, 36L, 42L, 43L, 45L, 46L, 58L, 48L, 37L, 37L, 40L, 42L, 51L, 
47L, 36L, 38L, 42L, 39L, 38L, 49L, 39L, 39L, 54L, 35L, 45L, 36L, 
52L, 53L, 41L, 48L, 48L, 41L, 41L, 42L, 36L, 47L, 38L, 48L, 42L, 
40L, 57L, 36L, 58L, 35L, 38L, 39L, 53L, 35L, 38L, 47L, 47L, 41L, 
53L, 54L, 39L, 38L, 38L, 37L, 42L, 37L, 36L, 60L, 54L, 41L, 40L, 
42L, 43L, 53L, 47L, 42L, 42L, 59L, 58L, 46L, 38L, 54L, 60L, 60L, 
39L, 59L, 37L, 46L, 46L, 42L, 41L, 58L, 42L, 48L, 44L, 49L, 57L, 
56L, 49L, 39L, 47L, 48L, 48L, 47L, 45L, 60L, 39L, 46L, 51L, 50L, 
36L, 49L), EstimatedSalary = c(19000L, 20000L, 43000L, 57000L, 
76000L, 58000L, 84000L, 150000L, 33000L, 65000L, 80000L, 52000L, 
86000L, 18000L, 82000L, 80000L, 25000L, 26000L, 28000L, 29000L, 
22000L, 49000L, 41000L, 22000L, 23000L, 20000L, 28000L, 30000L, 
43000L, 18000L, 74000L, 137000L, 16000L, 44000L, 90000L, 27000L, 
28000L, 49000L, 72000L, 31000L, 17000L, 51000L, 108000L, 15000L, 
84000L, 20000L, 79000L, 54000L, 135000L, 89000L, 32000L, 44000L, 
83000L, 23000L, 58000L, 55000L, 48000L, 79000L, 18000L, 117000L, 
20000L, 87000L, 66000L, 120000L, 83000L, 58000L, 19000L, 82000L, 
63000L, 68000L, 80000L, 27000L, 23000L, 113000L, 18000L, 112000L, 
52000L, 27000L, 87000L, 17000L, 80000L, 42000L, 49000L, 88000L, 
62000L, 118000L, 55000L, 85000L, 81000L, 50000L, 81000L, 116000L, 
15000L, 28000L, 83000L, 44000L, 25000L, 123000L, 73000L, 37000L, 
88000L, 59000L, 86000L, 149000L, 21000L, 72000L, 35000L, 89000L, 
86000L, 80000L, 71000L, 71000L, 61000L, 55000L, 80000L, 57000L, 
75000L, 52000L, 59000L, 59000L, 75000L, 72000L, 75000L, 53000L, 
51000L, 61000L, 65000L, 32000L, 17000L, 84000L, 58000L, 31000L, 
87000L, 68000L, 55000L, 63000L, 82000L, 107000L, 59000L, 25000L, 
85000L, 68000L, 59000L, 89000L, 25000L, 89000L, 96000L, 30000L, 
61000L, 74000L, 15000L, 45000L, 76000L, 50000L, 47000L, 15000L, 
59000L, 75000L, 30000L, 135000L, 100000L, 90000L, 33000L, 38000L, 
69000L, 86000L, 55000L, 71000L, 148000L, 47000L, 88000L, 115000L, 
118000L, 43000L, 72000L, 28000L, 47000L, 22000L, 23000L, 34000L, 
16000L, 71000L, 117000L, 43000L, 60000L, 66000L, 82000L, 41000L, 
72000L, 32000L, 84000L, 26000L, 43000L, 70000L, 89000L, 43000L, 
79000L, 36000L, 80000L, 22000L, 39000L, 74000L, 134000L, 71000L, 
101000L, 47000L, 130000L, 114000L, 142000L, 22000L, 96000L, 150000L, 
42000L, 58000L, 43000L, 108000L, 65000L, 78000L, 96000L, 143000L, 
80000L, 91000L, 144000L, 102000L, 60000L, 53000L, 126000L, 133000L, 
72000L, 80000L, 147000L, 42000L, 107000L, 86000L, 112000L, 79000L, 
57000L, 80000L, 82000L, 143000L, 149000L, 59000L, 88000L, 104000L, 
72000L, 146000L, 50000L, 122000L, 52000L, 97000L, 39000L, 52000L, 
134000L, 146000L, 44000L, 90000L, 72000L, 57000L, 95000L, 131000L, 
77000L, 144000L, 125000L, 72000L, 90000L, 108000L, 75000L, 74000L, 
144000L, 61000L, 133000L, 76000L, 42000L, 106000L, 26000L, 74000L, 
71000L, 88000L, 38000L, 36000L, 88000L, 61000L, 70000L, 21000L, 
141000L, 93000L, 62000L, 138000L, 79000L, 78000L, 134000L, 89000L, 
39000L, 77000L, 57000L, 63000L, 73000L, 112000L, 79000L, 117000L, 
38000L, 74000L, 137000L, 79000L, 60000L, 54000L, 134000L, 113000L, 
125000L, 50000L, 70000L, 96000L, 50000L, 141000L, 79000L, 75000L, 
104000L, 55000L, 32000L, 60000L, 138000L, 82000L, 52000L, 30000L, 
131000L, 60000L, 72000L, 75000L, 118000L, 107000L, 51000L, 119000L, 
65000L, 65000L, 60000L, 54000L, 144000L, 79000L, 55000L, 122000L, 
104000L, 75000L, 65000L, 51000L, 105000L, 63000L, 72000L, 108000L, 
77000L, 61000L, 113000L, 75000L, 90000L, 57000L, 99000L, 34000L, 
70000L, 72000L, 71000L, 54000L, 129000L, 34000L, 50000L, 79000L, 
104000L, 29000L, 47000L, 88000L, 71000L, 26000L, 46000L, 83000L, 
73000L, 130000L, 80000L, 32000L, 74000L, 53000L, 87000L, 23000L, 
64000L, 33000L, 139000L, 28000L, 33000L, 60000L, 39000L, 71000L, 
34000L, 35000L, 33000L, 23000L, 45000L, 42000L, 59000L, 41000L, 
23000L, 20000L, 33000L, 36000L), Purchased = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("0", 
"1"), class = "factor")), row.names = c(NA, -400L), class = "data.frame")
> dataset = read.csv('Social_Network_Ads.csv')
> dput(dataset)
structure(list(User.ID = c(15624510L, 15810944L, 15668575L, 15603246L, 
15804002L, 15728773L, 15598044L, 15694829L, 15600575L, 15727311L, 
15570769L, 15606274L, 15746139L, 15704987L, 15628972L, 15697686L, 
15733883L, 15617482L, 15704583L, 15621083L, 15649487L, 15736760L, 
15714658L, 15599081L, 15705113L, 15631159L, 15792818L, 15633531L, 
15744529L, 15669656L, 15581198L, 15729054L, 15573452L, 15776733L, 
15724858L, 15713144L, 15690188L, 15689425L, 15671766L, 15782806L, 
15764419L, 15591915L, 15772798L, 15792008L, 15715541L, 15639277L, 
15798850L, 15776348L, 15727696L, 15793813L, 15694395L, 15764195L, 
15744919L, 15671655L, 15654901L, 15649136L, 15775562L, 15807481L, 
15642885L, 15789109L, 15814004L, 15673619L, 15595135L, 15583681L, 
15605000L, 15718071L, 15679760L, 15654574L, 15577178L, 15595324L, 
15756932L, 15726358L, 15595228L, 15782530L, 15592877L, 15651983L, 
15746737L, 15774179L, 15667265L, 15655123L, 15595917L, 15668385L, 
15709476L, 15711218L, 15798659L, 15663939L, 15694946L, 15631912L, 
15768816L, 15682268L, 15684801L, 15636428L, 15809823L, 15699284L, 
15786993L, 15709441L, 15710257L, 15582492L, 15575694L, 15756820L, 
15766289L, 15593014L, 15584545L, 15675949L, 15672091L, 15801658L, 
15706185L, 15789863L, 15720943L, 15697997L, 15665416L, 15660200L, 
15619653L, 15773447L, 15739160L, 15689237L, 15679297L, 15591433L, 
15642725L, 15701962L, 15811613L, 15741049L, 15724423L, 15574305L, 
15678168L, 15697020L, 15610801L, 15745232L, 15722758L, 15792102L, 
15675185L, 15801247L, 15725660L, 15638963L, 15800061L, 15578006L, 
15668504L, 15687491L, 15610403L, 15741094L, 15807909L, 15666141L, 
15617134L, 15783029L, 15622833L, 15746422L, 15750839L, 15749130L, 
15779862L, 15767871L, 15679651L, 15576219L, 15699247L, 15619087L, 
15605327L, 15610140L, 15791174L, 15602373L, 15762605L, 15598840L, 
15744279L, 15670619L, 15599533L, 15757837L, 15697574L, 15578738L, 
15762228L, 15614827L, 15789815L, 15579781L, 15587013L, 15570932L, 
15794661L, 15581654L, 15644296L, 15614420L, 15609653L, 15594577L, 
15584114L, 15673367L, 15685576L, 15774727L, 15694288L, 15603319L, 
15759066L, 15814816L, 15724402L, 15571059L, 15674206L, 15715160L, 
15730448L, 15662067L, 15779581L, 15662901L, 15689751L, 15667742L, 
15738448L, 15680243L, 15745083L, 15708228L, 15628523L, 15708196L, 
15735549L, 15809347L, 15660866L, 15766609L, 15654230L, 15794566L, 
15800890L, 15697424L, 15724536L, 15735878L, 15707596L, 15657163L, 
15622478L, 15779529L, 15636023L, 15582066L, 15666675L, 15732987L, 
15789432L, 15663161L, 15694879L, 15593715L, 15575002L, 15622171L, 
15795224L, 15685346L, 15691808L, 15721007L, 15794253L, 15694453L, 
15813113L, 15614187L, 15619407L, 15646227L, 15660541L, 15753874L, 
15617877L, 15772073L, 15701537L, 15736228L, 15780572L, 15769596L, 
15586996L, 15722061L, 15638003L, 15775590L, 15730688L, 15753102L, 
15810075L, 15723373L, 15795298L, 15584320L, 15724161L, 15750056L, 
15609637L, 15794493L, 15569641L, 15815236L, 15811177L, 15680587L, 
15672821L, 15767681L, 15600379L, 15801336L, 15721592L, 15581282L, 
15746203L, 15583137L, 15680752L, 15688172L, 15791373L, 15589449L, 
15692819L, 15727467L, 15734312L, 15764604L, 15613014L, 15759684L, 
15609669L, 15685536L, 15750447L, 15663249L, 15638646L, 15734161L, 
15631070L, 15761950L, 15649668L, 15713912L, 15586757L, 15596522L, 
15625395L, 15760570L, 15566689L, 15725794L, 15673539L, 15705298L, 
15675791L, 15747043L, 15736397L, 15678201L, 15720745L, 15637593L, 
15598070L, 15787550L, 15603942L, 15733973L, 15596761L, 15652400L, 
15717893L, 15622585L, 15733964L, 15753861L, 15747097L, 15594762L, 
15667417L, 15684861L, 15742204L, 15623502L, 15774872L, 15611191L, 
15674331L, 15619465L, 15575247L, 15695679L, 15713463L, 15785170L, 
15796351L, 15639576L, 15693264L, 15589715L, 15769902L, 15587177L, 
15814553L, 15601550L, 15664907L, 15612465L, 15810800L, 15665760L, 
15588080L, 15776844L, 15717560L, 15629739L, 15729908L, 15716781L, 
15646936L, 15768151L, 15579212L, 15721835L, 15800515L, 15591279L, 
15587419L, 15750335L, 15699619L, 15606472L, 15778368L, 15671387L, 
15573926L, 15709183L, 15577514L, 15778830L, 15768072L, 15768293L, 
15654456L, 15807525L, 15574372L, 15671249L, 15779744L, 15624755L, 
15611430L, 15774744L, 15629885L, 15708791L, 15793890L, 15646091L, 
15596984L, 15800215L, 15577806L, 15749381L, 15683758L, 15670615L, 
15715622L, 15707634L, 15806901L, 15775335L, 15724150L, 15627220L, 
15672330L, 15668521L, 15807837L, 15592570L, 15748589L, 15635893L, 
15757632L, 15691863L, 15706071L, 15654296L, 15755018L, 15594041L
), Gender = structure(c(2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 
2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 
1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 
2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 
1L, 1L, 2L, 1L, 2L, 1L), .Label = c("Female", "Male"), class = "factor"), 
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    27L, 35L, 33L, 30L, 26L, 27L, 27L, 33L, 35L, 30L, 28L, 23L, 
    25L, 27L, 30L, 31L, 24L, 18L, 29L, 35L, 27L, 24L, 23L, 28L, 
    22L, 32L, 27L, 25L, 23L, 32L, 59L, 24L, 24L, 23L, 22L, 31L, 
    25L, 24L, 20L, 33L, 32L, 34L, 18L, 22L, 28L, 26L, 30L, 39L, 
    20L, 35L, 30L, 31L, 24L, 28L, 26L, 35L, 22L, 30L, 26L, 29L, 
    29L, 35L, 35L, 28L, 35L, 28L, 27L, 28L, 32L, 33L, 19L, 21L, 
    26L, 27L, 26L, 38L, 39L, 37L, 38L, 37L, 42L, 40L, 35L, 36L, 
    40L, 41L, 36L, 37L, 40L, 35L, 41L, 39L, 42L, 26L, 30L, 26L, 
    31L, 33L, 30L, 21L, 28L, 23L, 20L, 30L, 28L, 19L, 19L, 18L, 
    35L, 30L, 34L, 24L, 27L, 41L, 29L, 20L, 26L, 41L, 31L, 36L, 
    40L, 31L, 46L, 29L, 26L, 32L, 32L, 25L, 37L, 35L, 33L, 18L, 
    22L, 35L, 29L, 29L, 21L, 34L, 26L, 34L, 34L, 23L, 35L, 25L, 
    24L, 31L, 26L, 31L, 32L, 33L, 33L, 31L, 20L, 33L, 35L, 28L, 
    24L, 19L, 29L, 19L, 28L, 34L, 30L, 20L, 26L, 35L, 35L, 49L, 
    39L, 41L, 58L, 47L, 55L, 52L, 40L, 46L, 48L, 52L, 59L, 35L, 
    47L, 60L, 49L, 40L, 46L, 59L, 41L, 35L, 37L, 60L, 35L, 37L, 
    36L, 56L, 40L, 42L, 35L, 39L, 40L, 49L, 38L, 46L, 40L, 37L, 
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    44L, 37L, 48L, 37L, 50L, 52L, 41L, 40L, 58L, 45L, 35L, 36L, 
    55L, 35L, 48L, 42L, 40L, 37L, 47L, 40L, 43L, 59L, 60L, 39L, 
    57L, 57L, 38L, 49L, 52L, 50L, 59L, 35L, 37L, 52L, 48L, 37L, 
    37L, 48L, 41L, 37L, 39L, 49L, 55L, 37L, 35L, 36L, 42L, 43L, 
    45L, 46L, 58L, 48L, 37L, 37L, 40L, 42L, 51L, 47L, 36L, 38L, 
    42L, 39L, 38L, 49L, 39L, 39L, 54L, 35L, 45L, 36L, 52L, 53L, 
    41L, 48L, 48L, 41L, 41L, 42L, 36L, 47L, 38L, 48L, 42L, 40L, 
    57L, 36L, 58L, 35L, 38L, 39L, 53L, 35L, 38L, 47L, 47L, 41L, 
    53L, 54L, 39L, 38L, 38L, 37L, 42L, 37L, 36L, 60L, 54L, 41L, 
    40L, 42L, 43L, 53L, 47L, 42L, 42L, 59L, 58L, 46L, 38L, 54L, 
    60L, 60L, 39L, 59L, 37L, 46L, 46L, 42L, 41L, 58L, 42L, 48L, 
    44L, 49L, 57L, 56L, 49L, 39L, 47L, 48L, 48L, 47L, 45L, 60L, 
    39L, 46L, 51L, 50L, 36L, 49L), EstimatedSalary = c(19000L, 
    20000L, 43000L, 57000L, 76000L, 58000L, 84000L, 150000L, 
    33000L, 65000L, 80000L, 52000L, 86000L, 18000L, 82000L, 80000L, 
    25000L, 26000L, 28000L, 29000L, 22000L, 49000L, 41000L, 22000L, 
    23000L, 20000L, 28000L, 30000L, 43000L, 18000L, 74000L, 137000L, 
    16000L, 44000L, 90000L, 27000L, 28000L, 49000L, 72000L, 31000L, 
    17000L, 51000L, 108000L, 15000L, 84000L, 20000L, 79000L, 
    54000L, 135000L, 89000L, 32000L, 44000L, 83000L, 23000L, 
    58000L, 55000L, 48000L, 79000L, 18000L, 117000L, 20000L, 
    87000L, 66000L, 120000L, 83000L, 58000L, 19000L, 82000L, 
    63000L, 68000L, 80000L, 27000L, 23000L, 113000L, 18000L, 
    112000L, 52000L, 27000L, 87000L, 17000L, 80000L, 42000L, 
    49000L, 88000L, 62000L, 118000L, 55000L, 85000L, 81000L, 
    50000L, 81000L, 116000L, 15000L, 28000L, 83000L, 44000L, 
    25000L, 123000L, 73000L, 37000L, 88000L, 59000L, 86000L, 
    149000L, 21000L, 72000L, 35000L, 89000L, 86000L, 80000L, 
    71000L, 71000L, 61000L, 55000L, 80000L, 57000L, 75000L, 52000L, 
    59000L, 59000L, 75000L, 72000L, 75000L, 53000L, 51000L, 61000L, 
    65000L, 32000L, 17000L, 84000L, 58000L, 31000L, 87000L, 68000L, 
    55000L, 63000L, 82000L, 107000L, 59000L, 25000L, 85000L, 
    68000L, 59000L, 89000L, 25000L, 89000L, 96000L, 30000L, 61000L, 
    74000L, 15000L, 45000L, 76000L, 50000L, 47000L, 15000L, 59000L, 
    75000L, 30000L, 135000L, 100000L, 90000L, 33000L, 38000L, 
    69000L, 86000L, 55000L, 71000L, 148000L, 47000L, 88000L, 
    115000L, 118000L, 43000L, 72000L, 28000L, 47000L, 22000L, 
    23000L, 34000L, 16000L, 71000L, 117000L, 43000L, 60000L, 
    66000L, 82000L, 41000L, 72000L, 32000L, 84000L, 26000L, 43000L, 
    70000L, 89000L, 43000L, 79000L, 36000L, 80000L, 22000L, 39000L, 
    74000L, 134000L, 71000L, 101000L, 47000L, 130000L, 114000L, 
    142000L, 22000L, 96000L, 150000L, 42000L, 58000L, 43000L, 
    108000L, 65000L, 78000L, 96000L, 143000L, 80000L, 91000L, 
    144000L, 102000L, 60000L, 53000L, 126000L, 133000L, 72000L, 
    80000L, 147000L, 42000L, 107000L, 86000L, 112000L, 79000L, 
    57000L, 80000L, 82000L, 143000L, 149000L, 59000L, 88000L, 
    104000L, 72000L, 146000L, 50000L, 122000L, 52000L, 97000L, 
    39000L, 52000L, 134000L, 146000L, 44000L, 90000L, 72000L, 
    57000L, 95000L, 131000L, 77000L, 144000L, 125000L, 72000L, 
    90000L, 108000L, 75000L, 74000L, 144000L, 61000L, 133000L, 
    76000L, 42000L, 106000L, 26000L, 74000L, 71000L, 88000L, 
    38000L, 36000L, 88000L, 61000L, 70000L, 21000L, 141000L, 
    93000L, 62000L, 138000L, 79000L, 78000L, 134000L, 89000L, 
    39000L, 77000L, 57000L, 63000L, 73000L, 112000L, 79000L, 
    117000L, 38000L, 74000L, 137000L, 79000L, 60000L, 54000L, 
    134000L, 113000L, 125000L, 50000L, 70000L, 96000L, 50000L, 
    141000L, 79000L, 75000L, 104000L, 55000L, 32000L, 60000L, 
    138000L, 82000L, 52000L, 30000L, 131000L, 60000L, 72000L, 
    75000L, 118000L, 107000L, 51000L, 119000L, 65000L, 65000L, 
    60000L, 54000L, 144000L, 79000L, 55000L, 122000L, 104000L, 
    75000L, 65000L, 51000L, 105000L, 63000L, 72000L, 108000L, 
    77000L, 61000L, 113000L, 75000L, 90000L, 57000L, 99000L, 
    34000L, 70000L, 72000L, 71000L, 54000L, 129000L, 34000L, 
    50000L, 79000L, 104000L, 29000L, 47000L, 88000L, 71000L, 
    26000L, 46000L, 83000L, 73000L, 130000L, 80000L, 32000L, 
    74000L, 53000L, 87000L, 23000L, 64000L, 33000L, 139000L, 
    28000L, 33000L, 60000L, 39000L, 71000L, 34000L, 35000L, 33000L, 
    23000L, 45000L, 42000L, 59000L, 41000L, 23000L, 20000L, 33000L, 
    36000L), Purchased = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
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    1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 
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    1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 
    0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 
    0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 
    1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 
    1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 
    1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 
    1L)), class = "data.frame", row.names = c(NA, -400L))

Answer 1

要在函数调用中未提供网格时获取网格的最小值和最大值 cp，插入符适合具有 cp = 0 的 rpart 模型，并从拟合模型。从这个 table 中获取最小和最大 cp 值，并使用用户指定的 tuneLength.

从它们制作网格

这可以从插入符号中实现的source of the rpart model看出。

首先拟合一个cp = 0的模型并提取cp table

initialFit <- rpart::rpart(.outcome ~ .,
                           data = dat,
                           control = rpart::rpart.control(cp = 0))$cptable

initialFit <- initialFit[order(-initialFit[,"CP"]), , drop = FALSE]

以下部分指定搜索space

if(search == "grid") {
    if(nrow(initialFit) < len) {
         tuneSeq <- data.frame(cp = seq(min(initialFit[, "CP"]),
                               max(initialFit[, "CP"]),
                               length = len))
        } else tuneSeq <-  data.frame(cp = initialFit[1:len,"CP"])
                      colnames(tuneSeq) <- "cp"
        } else {
           tuneSeq <- data.frame(cp = unique(sample(initialFit[, "CP"],
                                 size = len, replace = TRUE)))
 }

如果指定了网格搜索并且tuneLength（上述函数中的 len 变量）如果大于初始拟合中的 cps 数，则从最小到最大 cp 形成网格，长度等于tuneLength.

data.frame(cp = seq(min(initialFit[, "CP"]),
                                   max(initialFit[, "CP"]),
                                   length = len))

决策树的“train”函数如何选择最佳曲调？

How does function `train` for decision tree work in selecting the Best Tune?

r

decision-tree

hyperparameters

r-caret