如何使用 'adaboost' 方法在 R 中的 Caret 和 fastAdaboost 包中构建分类树
How to use the 'adaboost' method to Build Classification Trees wthin the Caret and fastAdaboost Packages in R
问题
我正在尝试在 Caret 和 fastAdaboost 包中使用 'adaboost' 方法。我的 objective 是使用 R 中的机器学习技术为即将到来的大学项目构建一个 classification tree，我正在学习本教程 here。
对于这个模型 （见下文），我已经下载了库 caret 和 fastAdaboost 并且每当我尝试 运行 我的模型，我收到消息了。
Error: object 'model_adaboost' not found
我不明白这段代码有什么问题 （见下文），因为它与我的其他模型相同，我不知道为什么 R 找不到我的模型。
如果有人能伸出援手，非常感谢。
这些模型运行宁就好了：
#Random Forest
**# Train the model using rf
model_rf = train(Country ~., data=train.data, method='rf', metric=metric, tuneLength= tuneLength, trControl = fitControl)
model_rf
#Naive Bayes
nb_tune <- data.frame(usekernel = TRUE, fL = 0, adjust=seq(0, 5, by = 1))
model.nb1 = train(Country ~., data=train.data,'nb', trControl=fitControl, metric=metric, tuneLength=tuneLength, tuneGrid = nb_tune, laplace = 0:3)
model.nb1
我的数据框结构
data.frame': 367 obs. of 10 variables:
$ Country : Factor w/ 3 levels "Italy","Turkey",..: 2 3 1 3 2 3 3 2 3 3 ...
$ Low.Freq : num -0.1 0.381 0.705 0.441 -0.603 ...
$ High.Freq : num -0.503 0.96 -0.371 0.207 -0.336 ...
$ Peak.Freq : num -0.4751 -0.0966 -0.2089 -0.1952 -0.3184 ...
$ Delta.Freq : num -0.334 0.122 -0.567 -0.148 -0.132 ...
$ Delta.Time : num -0.445 1.565 -1.145 0.131 0.666 ...
$ Peak.Time : num 0.0289 0.1897 -0.4765 -0.029 0.1492 ...
$ Center.Freq: num -0.5294 -0.0507 -0.1589 -0.0819 -0.405 ...
$ Start.Freq : num 0.672 1.787 0.403 0.388 -1.068 ...
$ End.Freq : num -0.5393 -0.8247 -0.0148 -0.9138 0.0482 ...
R 代码
library(caret)
library(fastAdaboost)
#Data is 'Clusters_Dummy_2'
##Produce a new version of the dataframe 'Clusters_Dummy' with the rows shuffled
NewClusters=Cluster_Dummy_2[sample(1:nrow(Cluster_Dummy_2)),]
#Produce a dataframe
NewCluster<-as.data.frame(NewClusters)
#display
print(NewCluster)
#Check the structure of the data
str(NewCluster)
#Number of rows
nrow(NewCluster)
#Split the data frame into 70% to 30% train and test data
training.parameters <- Cluster_Dummy_2$Country %>%
createDataPartition(p = 0.7, list = FALSE)
train.data <- NewClusters[training.parameters, ]
test.data <- NewClusters[-training.parameters, ]
##Auxiliary function for controlling model fitting
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
## repeated ten times
repeats = 10,
classProbs = TRUE,
verbose = TRUE)
fitGrid_2 <- expand.grid(mfinal = (1:3)*3, # This is new!
maxdepth = c(1, 3), # ...and this
coeflearn = c("Breiman"),
iter=100) # ...and this
model_adaboost = train(Country ~ ., data=train.data, method='adaboost', tuneLength = tuneLength, metric=metric, trControl = fitControl,
tuneGrid=fitGrid_2, verbose=TRUE)
model_adaboost
数据
structure(list(Low.Freq = c(435L, 94103292L, 1L, 2688L, 8471L,
28818L, 654755585L, 468628164L, 342491L, 2288474L, 3915L, 411L,
267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L, 9544861L,
3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L, 2445L,
8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 605L,
9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L, 28934316L,
7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L, 6L, 90975L,
83103577L, 9529L, 229093L, 42810L, 5L, 18175302L, 1443751L, 5831L,
8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L, 7434328L,
82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L, 0L, 936779338L,
4885076L, 745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L,
4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L, 5690L, 56L,
3561L, 78738L, 1803363L, 809369L, 7131L, 0L), High.Freq = c(6071L,
3210L, 6L, 7306092L, 6919054L, 666399L, 78L, 523880161L, 4700783L,
4173830L, 30L, 811L, 341014L, 780L, 44749L, 91L, 201620707L,
74L, 1L, 65422L, 595L, 89093186L, 946520L, 6940919L, 655350L,
4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L, 90519642L, 984L,
0L, 296209525L, 487088392L, 5L, 894L, 529L, 5L, 99106L, 2L, 926017L,
9078L, 1L, 21L, 88601017L, 575770L, 48L, 8431L, 194L, 62324996L,
5L, 81L, 40634727L, 806901520L, 6818173L, 3501L, 91780L, 36106039L,
5834347L, 58388837L, 34L, 3280L, 6507606L, 19L, 402L, 584L, 76L,
4078684L, 199L, 6881L, 92251L, 81715L, 40L, 327L, 57764L, 97668898L,
2676483L, 76L, 4694L, 817120L, 51L, 116712L, 666L, 3L, 42841L,
9724L, 21L, 4L, 359L, 2604L, 22L, 30490L, 5640L, 34L, 51923625L,
35544L), Peak.Freq = c(87005561L, 9102L, 994839015L, 42745869L,
32840L, 62737133L, 2722L, 24L, 67404881L, 999242982L, 3048L,
85315406L, 703037627L, 331264L, 8403609L, 3934064L, 50578953L,
370110665L, 3414L, 12657L, 40L, 432L, 7707L, 214L, 68588962L,
69467L, 75L, 500297L, 704L, 1L, 102659072L, 60896923L, 4481230L,
94124925L, 60164619L, 447L, 580L, 8L, 172L, 9478521L, 20L, 53L,
3072127L, 2160L, 27301893L, 8L, 4263L, 508L, 712409L, 50677L,
522433683L, 112844L, 193385L, 458269L, 93578705L, 22093131L,
6L, 9L, 1690461L, 0L, 4L, 652847L, 44767L, 21408L, 5384L, 304L,
721L, 651147L, 2426L, 586L, 498289375L, 945L, 6L, 816L, 46207L,
39135L, 6621028L, 66905L, 26905085L, 4098L, 0L, 14L, 88L, 530L,
97809006L, 90L, 6L, 260792844L, 9L, 833205723L, 99467321L, 5L,
8455640L, 54090L, 2L, 309L, 299161148L, 4952L, 454824L), Delta.Freq = c(5L,
78L, 88553L, 794L, 5L, 3859122L, 782L, 36L, 8756801L, 243169338L,
817789L, 8792384L, 7431L, 626921743L, 9206L, 95789L, 7916L, 8143453L,
6L, 4L, 6363L, 181125L, 259618L, 6751L, 33L, 37960L, 0L, 2L,
599582228L, 565585L, 19L, 48L, 269450424L, 70676581L, 7830566L,
4L, 86484313L, 21L, 90899794L, 2L, 72356L, 574280L, 869544L,
73418L, 6468164L, 2259L, 5938505L, 31329L, 1249L, 354L, 8817L,
3L, 2568L, 82809L, 29836269L, 5230L, 37L, 33752014L, 79307L,
1736L, 8522076L, 40L, 2289135L, 862L, 801448L, 8026L, 5L, 15L,
4393771L, 405914L, 71098L, 950288L, 8319L, 1396973L, 832L, 70L,
1746L, 61907L, 8709547L, 300750537L, 45862L, 91417085L, 79892L,
47765L, 5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L,
72L, 136L, 509L, 232325L, 13128104L, 1692L, 8581L, 23L), Delta.Time = c(1361082L,
7926L, 499L, 5004L, 3494530L, 213L, 64551179L, 70L, 797L, 5L,
72588L, 86976L, 5163L, 635080L, 3L, 91L, 919806257L, 81443L,
3135427L, 4410972L, 5810L, 8L, 46603718L, 422L, 1083626L, 48L,
15699890L, 7L, 90167635L, 446459879L, 2332071L, 761660L, 49218442L,
381L, 46L, 493197L, 46L, 798597155L, 45342274L, 6265842L, 6L,
3445819L, 351L, 1761227L, 214L, 959L, 908996387L, 6L, 3855L,
9096604L, 152664L, 7970052L, 32366926L, 31L, 5201618L, 114L,
7806411L, 70L, 239L, 5065L, 2L, 1L, 14472831L, 122042249L, 8L,
495604L, 29L, 8965478L, 2875L, 959L, 39L, 9L, 690L, 933626665L,
85294L, 580093L, 95934L, 982058L, 65244056L, 137508L, 29L, 7621L,
7527L, 72L, 2L, 315L, 6L, 2413L, 8625150L, 51298109L, 851L, 890460L,
160736L, 6L, 850842734L, 2L, 7L, 76969113L, 190536L), Peak.Time = c(1465265L,
452894L, 545076172L, 8226275L, 5040875L, 700530L, 1L, 3639L,
20141L, 71712131L, 686L, 923L, 770569738L, 69961L, 737458636L,
122403L, 199502046L, 6108L, 907L, 108078263L, 7817L, 4L, 6L,
69L, 721L, 786353L, 87486L, 1563L, 876L, 47599535L, 79295722L,
53L, 7378L, 591L, 6607935L, 954L, 6295L, 75514344L, 5742050L,
25647276L, 449L, 328566184L, 4L, 2L, 2703L, 21367543L, 63429043L,
708L, 782L, 909820L, 478L, 50L, 922L, 579882L, 7850L, 534L, 2157492L,
96L, 6L, 716L, 5L, 653290336L, 447854237L, 2L, 31972263L, 645L,
7L, 609909L, 4054695L, 455631L, 4919894L, 9L, 72713L, 9997L,
84090765L, 89742L, 5L, 5028L, 4126L, 23091L, 81L, 239635020L,
3576L, 898597785L, 6822L, 3798L, 201999L, 19624L, 20432923L,
18944093L, 930720236L, 1492302L, 300122L, 143633L, 5152743L,
417344L, 813L, 55792L, 78L), Center_Freq = c(61907L, 8709547L,
300750537L, 45862L, 91417085L, 79892L, 47765L, 5477L, 18L, 4186L,
2860L, 754038591L, 375L, 53809223L, 72L, 136L, 4700783L, 4173830L,
30L, 811L, 341014L, 780L, 44749L, 91L, 201620707L, 74L, 1L, 65422L,
595L, 89093186L, 946520L, 6940919L, 48744L, 2317845L, 5126197L,
2445L, 8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L,
9L, 651547554L, 45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L,
3001L, 9L, 3270L, 141L, 53644L, 667983L, 565598L, 84L, 971L,
555498297L, 60431L, 6597L, 856943893L, 607815536L, 4406L, 79L,
4885076L, 745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L,
4440739L, 754038591L, 375L, 53809223L, 72L, 136L, 509L, 232325L,
13128104L, 1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L,
5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L, 21387L
), Start.Freq = c(426355L, 22073538L, 680374L, 41771L, 54L, 6762844L,
599171L, 108L, 257451851L, 438814L, 343045L, 4702L, 967787L,
1937L, 18L, 89301735L, 366L, 90L, 954L, 7337732L, 70891703L,
4139L, 10397931L, 940000382L, 7L, 38376L, 878528819L, 6287L,
738366L, 31L, 47L, 5L, 6L, 77848L, 2366508L, 45L, 3665842L, 7252260L,
6L, 61L, 3247L, 448348L, 1L, 705132L, 144L, 7423637L, 2L, 497L,
844927639L, 78978L, 914L, 131L, 7089563L, 927L, 9595581L, 2774463L,
1651L, 73509280L, 7L, 35L, 18L, 96L, 1L, 92545512L, 27354947L,
7556L, 65019L, 7480L, 71835L, 8249L, 64792L, 71537L, 349389666L,
280244484L, 82L, 6L, 40L, 353872L, 0L, 103L, 1255L, 4752L, 29L,
76L, 81185L, 14L, 9L, 470775630L, 818361265L, 57947209L, 44L,
24L, 41295L, 4L, 261449L, 9931404L, 773556640L, 930717L, 65007421L
), End.Freq = c(71000996L, 11613579L, 71377155L, 1942738L, 8760748L,
79L, 455L, 374L, 8L, 5L, 2266932L, 597833L, 155488L, 3020L, 4L,
554L, 4L, 16472L, 1945649L, 668181101L, 649780L, 22394365L, 93060602L,
172146L, 20472L, 23558847L, 190513L, 22759044L, 44L, 78450L,
205621181L, 218L, 69916344L, 23884L, 66L, 312148L, 7710564L,
4L, 422L, 744572L, 651547554L, 45554L, 38493L, 91055218L, 38L,
1116474L, 2295482L, 3001L, 9L, 3270L, 141L, 55595L, 38451L, 8660867L,
14L, 96L, 345L, 6L, 44L, 8235824L, 910517L, 1424326L, 87102566L,
53644L, 667983L, 565598L, 84L, 971L, 555498297L, 60431L, 6597L,
856943893L, 607815536L, 4406L, 79L, 7L, 28978746L, 7537295L,
6L, 633L, 345860066L, 802L, 1035131L, 602L, 2740L, 8065L, 61370968L,
429953765L, 981507L, 8105L, 343787257L, 44782L, 64184L, 12981359L,
123367978L, 818775L, 123745614L, 25345654L, 3L), Country = c("Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Spain",
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain",
"Spain", "Spain", "Spain", "Spain", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "France", "France", "France",
"France", "France", "France", "France", "France", "France", "France",
"France", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain",
"Spain", "Spain", "France", "France", "France", "France", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain",
"Holland", "Holland", "Holland", "Holland", "France", "France",
"France", "France", "France", "France", "France", "Spain", "Spain",
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain",
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain",
"Spain", "Spain", "France", "France", "France")), row.names = c(NA,
99L), class = "data.frame")
[1]: https://www.machinelearningplus.com/machine-learning/caret-package/https://
这里发生了一些事情。首先，您没有定义 metric，并且执行代码也说明了这一点。我将在下面使用默认指标。其次，正如我在评论中建议的那样，adaboost 模型中只有两个调整参数，nIter 和 method（"Adaboost.MI" 或 "Real Adaboost"）。因此，我们可以将调整网格更改为以下内容：
fitGrid_2 <- expand.grid(nIter = seq(10, 100, by=10),
method=c("Adaboost.MI", "Real Adaboost"))
现在，当我们 运行 模型时：
model_adaboost = train(Country ~ .,
data=train.data,
method='adaboost',
tuneLength = tuneLength,
trControl = fitControl,
tuneGrid=fitGrid_2,
verbose=TRUE)
我们收到大量警告和空结果。警告都是这样的：
50: model fit failed for Fold03.Rep01: nIter=100, method=Adaboost.MI Error : Dependent variables must have two levels
这表明adaboost要求分类任务只有两种可能性。如果我们只看两个国家，它是有效的：
NewClusters2 <- subset(NewClusters, Country %in% c("France", "Spain"))
training.parameters <- NewClusters2$Country %>%
createDataPartition(p = 0.7, list = FALSE)
train.data <- NewClusters2[training.parameters, ]
test.data <- NewClusters2[-training.parameters, ]
model_adaboost = train(Country ~ .,
data=train.data,
method='adaboost',
tuneLength = tuneLength,
trControl = fitControl,
tuneGrid=fitGrid_2,
verbose=TRUE)
问题
我正在尝试在 Caret 和 fastAdaboost 包中使用 'adaboost' 方法。我的 objective 是使用 R 中的机器学习技术为即将到来的大学项目构建一个 classification tree，我正在学习本教程 here。
对于这个模型 （见下文），我已经下载了库 caret 和 fastAdaboost 并且每当我尝试 运行 我的模型，我收到消息了。
Error: object 'model_adaboost' not found
我不明白这段代码有什么问题 （见下文），因为它与我的其他模型相同，我不知道为什么 R 找不到我的模型。
如果有人能伸出援手，非常感谢。
这些模型运行宁就好了：
#Random Forest
**# Train the model using rf
model_rf = train(Country ~., data=train.data, method='rf', metric=metric, tuneLength= tuneLength, trControl = fitControl)
model_rf
#Naive Bayes
nb_tune <- data.frame(usekernel = TRUE, fL = 0, adjust=seq(0, 5, by = 1))
model.nb1 = train(Country ~., data=train.data,'nb', trControl=fitControl, metric=metric, tuneLength=tuneLength, tuneGrid = nb_tune, laplace = 0:3)
model.nb1
我的数据框结构
data.frame': 367 obs. of 10 variables:
$ Country : Factor w/ 3 levels "Italy","Turkey",..: 2 3 1 3 2 3 3 2 3 3 ...
$ Low.Freq : num -0.1 0.381 0.705 0.441 -0.603 ...
$ High.Freq : num -0.503 0.96 -0.371 0.207 -0.336 ...
$ Peak.Freq : num -0.4751 -0.0966 -0.2089 -0.1952 -0.3184 ...
$ Delta.Freq : num -0.334 0.122 -0.567 -0.148 -0.132 ...
$ Delta.Time : num -0.445 1.565 -1.145 0.131 0.666 ...
$ Peak.Time : num 0.0289 0.1897 -0.4765 -0.029 0.1492 ...
$ Center.Freq: num -0.5294 -0.0507 -0.1589 -0.0819 -0.405 ...
$ Start.Freq : num 0.672 1.787 0.403 0.388 -1.068 ...
$ End.Freq : num -0.5393 -0.8247 -0.0148 -0.9138 0.0482 ...
R 代码
library(caret)
library(fastAdaboost)
#Data is 'Clusters_Dummy_2'
##Produce a new version of the dataframe 'Clusters_Dummy' with the rows shuffled
NewClusters=Cluster_Dummy_2[sample(1:nrow(Cluster_Dummy_2)),]
#Produce a dataframe
NewCluster<-as.data.frame(NewClusters)
#display
print(NewCluster)
#Check the structure of the data
str(NewCluster)
#Number of rows
nrow(NewCluster)
#Split the data frame into 70% to 30% train and test data
training.parameters <- Cluster_Dummy_2$Country %>%
createDataPartition(p = 0.7, list = FALSE)
train.data <- NewClusters[training.parameters, ]
test.data <- NewClusters[-training.parameters, ]
##Auxiliary function for controlling model fitting
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
## repeated ten times
repeats = 10,
classProbs = TRUE,
verbose = TRUE)
fitGrid_2 <- expand.grid(mfinal = (1:3)*3, # This is new!
maxdepth = c(1, 3), # ...and this
coeflearn = c("Breiman"),
iter=100) # ...and this
model_adaboost = train(Country ~ ., data=train.data, method='adaboost', tuneLength = tuneLength, metric=metric, trControl = fitControl,
tuneGrid=fitGrid_2, verbose=TRUE)
model_adaboost
数据
structure(list(Low.Freq = c(435L, 94103292L, 1L, 2688L, 8471L,
28818L, 654755585L, 468628164L, 342491L, 2288474L, 3915L, 411L,
267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L, 9544861L,
3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L, 2445L,
8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 605L,
9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L, 28934316L,
7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L, 6L, 90975L,
83103577L, 9529L, 229093L, 42810L, 5L, 18175302L, 1443751L, 5831L,
8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L, 7434328L,
82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L, 0L, 936779338L,
4885076L, 745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L,
4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L, 5690L, 56L,
3561L, 78738L, 1803363L, 809369L, 7131L, 0L), High.Freq = c(6071L,
3210L, 6L, 7306092L, 6919054L, 666399L, 78L, 523880161L, 4700783L,
4173830L, 30L, 811L, 341014L, 780L, 44749L, 91L, 201620707L,
74L, 1L, 65422L, 595L, 89093186L, 946520L, 6940919L, 655350L,
4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L, 90519642L, 984L,
0L, 296209525L, 487088392L, 5L, 894L, 529L, 5L, 99106L, 2L, 926017L,
9078L, 1L, 21L, 88601017L, 575770L, 48L, 8431L, 194L, 62324996L,
5L, 81L, 40634727L, 806901520L, 6818173L, 3501L, 91780L, 36106039L,
5834347L, 58388837L, 34L, 3280L, 6507606L, 19L, 402L, 584L, 76L,
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[1]: https://www.machinelearningplus.com/machine-learning/caret-package/https://
这里发生了一些事情。首先，您没有定义 metric，并且执行代码也说明了这一点。我将在下面使用默认指标。其次，正如我在评论中建议的那样，adaboost 模型中只有两个调整参数，nIter 和 method（"Adaboost.MI" 或 "Real Adaboost"）。因此，我们可以将调整网格更改为以下内容：
fitGrid_2 <- expand.grid(nIter = seq(10, 100, by=10),
method=c("Adaboost.MI", "Real Adaboost"))
现在，当我们 运行 模型时：
model_adaboost = train(Country ~ .,
data=train.data,
method='adaboost',
tuneLength = tuneLength,
trControl = fitControl,
tuneGrid=fitGrid_2,
verbose=TRUE)
我们收到大量警告和空结果。警告都是这样的：
50: model fit failed for Fold03.Rep01: nIter=100, method=Adaboost.MI Error : Dependent variables must have two levels
这表明adaboost要求分类任务只有两种可能性。如果我们只看两个国家，它是有效的：
NewClusters2 <- subset(NewClusters, Country %in% c("France", "Spain"))
training.parameters <- NewClusters2$Country %>%
createDataPartition(p = 0.7, list = FALSE)
train.data <- NewClusters2[training.parameters, ]
test.data <- NewClusters2[-training.parameters, ]
model_adaboost = train(Country ~ .,
data=train.data,
method='adaboost',
tuneLength = tuneLength,
trControl = fitControl,
tuneGrid=fitGrid_2,
verbose=TRUE)