Question

到目前为止，我正在使用带有RandomForest的CARET包进行训练。

我使用CARET的train函数进行交叉验证，一切运行良好。

直到我想尝试使用神经网络并上传RSNNS包。现在，每当我尝试使用火车（使用我的旧rf算法）时，我都会收到以下错误：

UseMethod出错（＆＃34; train＆＃34;）：没有适用的方法来训练＆＃39;应用于课程＆＃34; c的对象（＆＃39; tbl_df＆＃39;，＆＃39; tbl＆＃39;，＆＃39; data.frame＆＃39;）＆＃34;

那是错误吗？为什么RSNNS会导致这种情况？

Answer 1

问题是RSNNS::train()屏蔽了caret::train()，因为RSNNS版本是在插入符号后加载的。通过使用caret::train()语法调用packageName::function()来解决此问题。

library(caret)
library(RSNNS)

library(mlbench)
data(Sonar)

inTraining <- createDataPartition(Sonar$Class, p = .75, list=FALSE)
training <- Sonar[inTraining,]
testing <- Sonar[-inTraining,]
fitControl <- trainControl(method = "cv",
                           number = 3)
# error because RSNNS::train does not work like caret::train()
system.time(fit <- train(Class ~ ., method="rf",data=Sonar,trControl = fitControl))
# correct by calling caret::train()
system.time(fit <- caret::train(Class ~ ., method="rf",data=Sonar,trControl = fitControl))
fit

...和输出：

> system.time(fit <- train(Cx=Sonar[,-61],y=Sonar[,61], method="rf",data=Sonar,trControl = fitControl))
Error in UseMethod("train") : 
  no applicable method for 'train' applied to an object of class "data.frame"
Timing stopped at: 0.033 0 0.034
> # correct by calling caret::train()
> system.time(fit <- caret::train(x=Sonar[,-61],y=Sonar[,61], method="rf",data=Sonar,trControl = fitControl))
   user  system elapsed 
  3.888   0.069   3.981 
> fit
Random Forest 

208 samples
 60 predictor
  2 classes: 'M', 'R' 

No pre-processing
Resampling: Cross-Validated (3 fold) 
Summary of sample sizes: 139, 138, 139 
Resampling results across tuning parameters:

  mtry  Accuracy   Kappa    
   2    0.8175983  0.6292393
  31    0.7645963  0.5249374
  60    0.7694272  0.5336925

Accuracy was used to select the optimal model using the largest value.
The final value used for the model was mtry = 2.
>

＆＃34; RSNNS＆＃34;包裹不允许我使用CARET的列车功能

1 个答案: