r - 尝试使用 R 中的 RWeka 包应用决策 C4.5 算法时出错

Question

我正在尝试使用决策树 C 4.5 算法和 10 折交叉验证来检测网络垃圾邮件。在进行特征选择后，我的数据集基本上有 8944 个观测值和 36 个变量。

这是我的代码:

#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 ))

这是我收到的错误:

C45Fit<-train(spam~.,method="J48",data=trainData,
               tuneLength=15,
               trControl=trainControl(
               method="cv",indexOut = train_control ))

Error in train(spam ~ ., method = "J48", data = trainData, tuneLength = 15, : unused arguments (method = "J48", data = trainData, tuneLength = 15, trControl = trainControl(method = "cv", indexOut = train_control))

我有几个问题:

1. 如何解决此错误？

2. 如何设置tuneLength参数？

我的数据集的负责人:

> 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()的输出

> 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       

感谢您提前提供的任何建议。

Answer 1

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))

注意消息以下对象被“package:caret”屏蔽:train。如果在加载 caret 后加载另一个带有 train 函数的包(例如本例中的 mlr)，默认情况下 R 将使用 >train 从最近加载的包中。 (这就是为什么我要求使用 sessionInfo() 来查看已加载的包。出于同样的原因，可复制的示例应该包含您加载的包。)而不是 train 来自 caret，R 从 mlr(或您加载的其他包)运行 train，这会返回错误消息。

解决方案是最后加载 caret，或者使用 caret::train( 从 caret 显式调用 train 函数...).