r - 使用 caret 包的变量重要性(错误)；随机森林算法

Question

我试图以任何方式获得 rf 模型的可变重要性。这是我迄今为止尝试过的方法，但非常欢迎其他建议。

我在 R 中训练了一个模型:

require(caret)
require(randomForest)
myControl = trainControl(method='cv',number=5,repeats=2,returnResamp='none')
model2 = train(increaseInAssessedLevel~., data=trainData, method = 'rf', trControl=myControl)

数据集相当大，但模型运行良好。我可以访问它的部分并运行命令，例如:

> model2[3]
$results
  mtry      RMSE  Rsquared      RMSESD RsquaredSD
1    2 0.1901304 0.3342449 0.004586902 0.05089500
2   61 0.1080164 0.6984240 0.006195397 0.04428158
3  120 0.1084201 0.6954841 0.007119253 0.04362755

但我收到以下错误:

> varImp(model2)
Error in varImp[, "%IncMSE"] : subscript out of bounds

显然应该有一个包装器，但情况似乎并非如此:(cf: http://www.inside-r.org/packages/cran/caret/docs/varImp )

varImp.randomForest(model2)
Error: could not find function "varImp.randomForest"

但这特别奇怪:

> traceback()
No traceback available 

> sessionInfo()
R version 3.0.1 (2013-05-16)
Platform: x86_64-redhat-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=C                 LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] elasticnet_1.1     lars_1.2           klaR_0.6-9         MASS_7.3-26       
 [5] kernlab_0.9-18     nnet_7.3-6         randomForest_4.6-7 doMC_1.3.0        
 [9] iterators_1.0.6    caret_5.17-7       reshape2_1.2.2     plyr_1.8          
[13] lattice_0.20-15    foreach_1.4.1      cluster_1.14.4    

loaded via a namespace (and not attached):
[1] codetools_0.2-8 compiler_3.0.1  grid_3.0.1      stringr_0.6.2  
[5] tools_3.0.1  

Answer 1

重要性分数可能需要一段时间来计算和 train不会自动获得 randomForest创建它们。添加 importance = TRUE到 train打电话，它应该工作。

最大限度