使用 caret 包的变量重要性(错误);随机森林算法 [英] Variable importance using the caret package (error); RandomForest algorithm

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问题描述

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

I am trying to obtain the variable importance of a rf model in any way. This is the approach I have tried so far, but alternate suggestions are very welcome.

我已经用 R 训练了一个模型:

I have trained a model in R:

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:

The dataset is fairly large, but the model runs fine. I can access its parts and run commands such as:

> 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)

Apparently there is supposed to be a wrapper, but that does not seem to be the case: (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  

推荐答案

计算重要性分数可能需要一段时间,train 不会自动获得 randomForest 来创建他们.将 importance = TRUE 添加到 train 调用中,它应该可以工作.

The importance scores can take a while to compute and train won't automatically get randomForest to create them. Add importance = TRUE to the train call and it should work.

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