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
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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