尝试使用R中的RWeka包应用决策C4.5算法时出错

我正在尝试使用决策树C4.5算法和10倍交叉验证来检测Web垃圾邮件。在进行特征选择后，我的数据集基本上有8944个观察值和36个变量

这是我的密码：

#dividing the dataset into train and test
trainRowNumbers<-createDataPartition(final1$spam,p=0.7,list=FALSE)
#Create the training dataset
trainData<-final1[trainRowNumbers,]
#Create Test data
testData<-final1[-trainRowNumbers,]

#C4.5 using 10 fold cross validation
set.seed(1958)
train_control<-createFolds(trainData$spam,k=10)
C45Fit<-train(spam~.,method="J48",data=trainData,
              tuneLength=15,
              trControl=trainControl(
               method="cv",indexOut = train_control ))

sessionInfo（）的输出

---

感谢您提前提供的任何建议。

我可以通过以下方式复制错误消息：

library(RWeka)
library(caret)
library(mlr)
# Loading required package: ParamHelpers

# Attaching package: ‘mlr’

# The following object is masked from ‘package:caret’:

#     train
#dividing the dataset into train and test
trainRowNumbers <- createDataPartition(iris$Species, p = 0.7, list = FALSE)

#Create the training dataset
trainData <- iris[trainRowNumbers, ]
#Create Test data
testData <- iris[-trainRowNumbers, ]

#C4.5 using 10 fold cross validation
set.seed(1958)
train_control <- createFolds(trainData$Species, k = 10)
C45Fit <- train(Species~., method = "J48",data = trainData,
              tuneLength = 15,
              trControl = trainControl(
               method = "cv",indexOut = train_control ))
# Error in train(Species ~ ., method = "J48", data = trainData, tuneLength = 15,  : 
#   unused arguments (method = "J48", data = trainData, tuneLength = 15, trControl = trainControl(method = "cv", indexOut = train_control))

库（RWeka）
图书馆（插入符号）
图书馆（mlr）
#正在加载所需的包：ParamHelpers
#附加程序包：“mlr”
#以下对象被“package:caret”屏蔽：
#训练
#将数据集划分为训练和测试
TrainRowNumber示例中的错误是不可复制的；当我在代码中使用
iris
数据集时，它工作得非常好。你能提供一个可复制的例子吗？另外，您可以共享来自
sessionInfo（）
的输出吗？嘿！我已经附上了一张我的数据集外观的照片，还按照你的要求附上了sessionInfo（）的输出。Excel截图实际上没有帮助。一个可复制的示例是使用每个人都拥有的数据集，例如R附带的
iris
数据集。请参见。对此表示抱歉。再次编辑：）稍微好一点，但仍然没有真正的帮助。一个可复制的示例意味着任何人都可以复制和粘贴代码并在自己的计算机上运行，并获得与您相同的输出。请看下面我的答案。非常感谢。这奏效了。。C45Fit
> head(trainData)
  hostid                           host      HST_4     HST_6     HST_7     HST_8     HST_9    HST_10     HST_16
1      0         007cleaningagent.co.uk 0.03370787 1.9791304 0.1123596 0.1516854 0.2247191 0.2977528 0.07865169
2      1           0800.loan-line.co.uk 1.39539347 2.4222020 0.2284069 0.2610365 0.3531670 0.4529750 0.02879079
4      3 102belfast.boys-brigade.org.uk 0.29729730 1.1800000 0.2162162 0.3783784 0.5135135 0.5405405 0.21621622
5      4  10bristol.boys-brigade.org.uk 0.28804348 1.7745267 0.1141304 0.1847826 0.2608696 0.3750000 0.08152174
6      5  10enfield.boys-brigade.org.uk 0.00000000 0.8468468 0.0625000 0.1875000 0.1875000 0.3125000 0.06250000
8      8             13thcoventry.co.uk 0.05797101 2.1113074 0.2318841 0.3091787 0.3961353 0.5507246 0.09178744
      HST_17    HST_18 HST_20    HMG_29     HMG_40     HMG_41    HMG_42    AVG_50    AVG_51     AVG_55    AVG_57
1 0.15730337 0.2247191  0.070 0.2907760 0.02702703 0.07207207 0.1351351  32431.65  7.215054 0.02289305 0.2980171
2 0.05566219 0.1094050  0.075 0.0495162 0.10641628 0.17840376 0.2410016 150592.89  2.000000 0.49661240 0.1137439
4 0.37837838 0.4054054  0.040 0.2156130 0.03971119 0.11552347 0.1480144  16129.61  2.125000 0.12297815 0.2033877
5 0.13043478 0.2119565  0.075 0.0405612 0.08152174 0.13043478 0.2119565  28759.75  2.870968 0.19622331 0.0673372
6 0.18750000 0.2500000  0.005 0.1125400 0.02528090 0.12359551 0.1432584  70966.61  2.000000 0.03948338 0.2513755
8 0.14975845 0.2512077  0.095 0.1946150 0.04382470 0.10458167 0.1633466 109388.89 11.484940 0.03547817 0.1387366
       AVG_58   AVG_59     AVG_61     AVG_63    AVG_65    AVG_67     STD_77     STD_79       STD_80     STD_81
1 0.030079101 1.888686 0.04982536 0.07119317 0.1539772 0.2237475 0.02240051 0.04634758 0.0003248904 0.07644575
2 0.005874481 2.423238 0.14016213 0.17484142 0.2460647 0.3279534 0.03014901 0.05352347 0.0006170884 0.09449420
4 0.017285860 1.657795 0.08748573 0.14192639 0.2273218 0.2815660 0.03715705 0.07385004 0.0021174754 0.15725521
5 0.007008439 1.656472 0.10088409 0.17370255 0.2791502 0.3839271 0.03382564 0.07695898 0.0011314215 0.14290420
6 0.017145414 2.284363 0.09245673 0.14045514 0.2267635 0.2907555 0.02459505 0.06418522 0.0007756064 0.16533374
8 0.001818059 2.300361 0.17326186 0.25910768 0.3351511 0.4479340 0.05611160 0.07531329 0.0005475770 0.15796253
     STD_83      STD_84     STD_85     STD_87    STD_94   spam
1 0.1219990 0.001009964 0.04043011 0.04198925 0.3400028 normal
2 0.1539489 0.001734261 0.15000000 0.16000000 0.3147682 normal
4 0.2027374 0.006655953 0.06437500 0.06031250 0.7100778 normal
5 0.1925378 0.002708827 0.04258065 0.05290323 0.8195509 normal
6 0.2223814 0.005491305 0.09125000 0.08062500 1.2953592 normal
8 0.2366591 0.002588343 0.21698795 0.14774096 0.2882247 normal

> sessionInfo()
R version 3.4.0 (2017-04-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252    LC_MONETARY=English_Australia.1252
[4] LC_NUMERIC=C                       LC_TIME=English_Australia.1252    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2        ggthemes_3.5.0      randomForest_4.6-12 Metrics_0.1.3       RWeka_0.4-37        mlr_2.12.1         
 [7] ParamHelpers_1.10   rgeos_0.3-26        VIM_4.7.0           data.table_1.10.4-3 colorspace_1.3-2    mice_2.46.0        
[13] RANN_2.5.1          kernlab_0.9-25      mlbench_2.1-1       caret_6.0-79        ggplot2_2.2.1       lattice_0.20-35    
[19] dplyr_0.7.4        

loaded via a namespace (and not attached):
 [1] nlme_3.1-131       lubridate_1.7.3    bit64_0.9-7        dimRed_0.1.0       httr_1.3.1         backports_1.1.2    tools_3.4.0       
 [8] R6_2.2.2           rpart_4.1-11       DBI_0.8            lazyeval_0.2.1     nnet_7.3-12        withr_2.1.0        sp_1.2-7          
[15] tidyselect_0.2.3   mnormt_1.5-5       parallelMap_1.3    bit_1.1-12         curl_3.0           compiler_3.4.0     checkmate_1.8.5   
[22] scales_0.5.0       sfsmisc_1.1-1      DEoptimR_1.0-8     lmtest_0.9-35      psych_1.7.8        robustbase_0.92-8  stringr_1.2.0     
[29] foreign_0.8-67     rio_0.5.10         pkgconfig_2.0.1    RWekajars_3.9.2-1  rlang_0.2.0        readxl_1.0.0       ddalpha_1.3.1     
[36] BBmisc_1.11        bindr_0.1          zoo_1.8-0          ModelMetrics_1.1.0 car_3.0-0          magrittr_1.5       Matrix_1.2-12     
[43] Rcpp_0.12.14       munsell_0.4.3      abind_1.4-5        stringi_1.1.6      carData_3.0-1      MASS_7.3-47        plyr_1.8.4        
[50] recipes_0.1.1      parallel_3.4.0     forcats_0.3.0      haven_1.1.1        splines_3.4.0      pillar_1.2.1       boot_1.3-19       
[57] rjson_0.2.15       reshape2_1.4.2     codetools_0.2-15   stats4_3.4.0       CVST_0.2-1         glue_1.2.0         laeken_0.4.6      
[64] vcd_1.4-4          foreach_1.4.3      twitteR_1.1.9      cellranger_1.1.0   gtable_0.2.0       purrr_0.2.4        tidyr_0.7.2       
[71] assertthat_0.2.0   DRR_0.0.2          gower_0.1.2        openxlsx_4.0.17    prodlim_1.6.1      broom_0.4.3        e1071_1.6-8       
[78] class_7.3-14       survival_2.41-3    timeDate_3042.101  RcppRoll_0.2.2     tibble_1.4.2       rJava_0.9-9        iterators_1.0.8   
[85] lava_1.5.1         ipred_0.9-6       

library(RWeka)
library(caret)
library(mlr)
# Loading required package: ParamHelpers

# Attaching package: ‘mlr’

# The following object is masked from ‘package:caret’:

#     train
#dividing the dataset into train and test
trainRowNumbers <- createDataPartition(iris$Species, p = 0.7, list = FALSE)

#Create the training dataset
trainData <- iris[trainRowNumbers, ]
#Create Test data
testData <- iris[-trainRowNumbers, ]

#C4.5 using 10 fold cross validation
set.seed(1958)
train_control <- createFolds(trainData$Species, k = 10)
C45Fit <- train(Species~., method = "J48",data = trainData,
              tuneLength = 15,
              trControl = trainControl(
               method = "cv",indexOut = train_control ))
# Error in train(Species ~ ., method = "J48", data = trainData, tuneLength = 15,  : 
#   unused arguments (method = "J48", data = trainData, tuneLength = 15, trControl = trainControl(method = "cv", indexOut = train_control))