Question

我使用caret + ranger训练了一个随机森林。

fit <- train(
    y ~ x1 + x2
    ,data = total_set
    ,method = "ranger"
    ,trControl = trainControl(method="cv", number = 5, allowParallel = TRUE, verbose = TRUE)
    ,tuneGrid = expand.grid(mtry = c(4,5,6))
    ,importance = 'impurity'
)

现在我想看看变量的重要性。但是，这些都不起作用：

> importance(fit)
Error in UseMethod("importance") : no applicable method for 'importance' applied to an object of class "c('train', 'train.formula')"
> fit$variable.importance
NULL
> fit$importance
NULL

> fit
Random Forest 

217380 samples
    32 predictors

No pre-processing
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 173904, 173904, 173904, 173904, 173904 
Resampling results across tuning parameters:

  mtry  RMSE        Rsquared 
  4     0.03640464  0.5378731
  5     0.03645528  0.5366478
  6     0.03651451  0.5352838

RMSE was used to select the optimal model using  the smallest value.
The final value used for the model was mtry = 4.

任何想法是否＆amp;我怎么能得到它？

感谢。

Answer 1

varImp(fit)会为您提供。

为了解决这个问题，我查看了names(fit)，这导致我names(fit$modelInfo) - 然后您会看到varImp作为其中一个选项。

Answer 2

根据@fmalaussena

set.seed(123)
ctrl <- trainControl(method = 'cv', 
                     number = 10,
                     classProbs = TRUE,
                     savePredictions = TRUE,
                     verboseIter = TRUE)

rfFit <- train(Species ~ ., 
               data = iris, 
               method = "ranger",
               importance = "permutation", #***
               trControl = ctrl,
               verbose = T)

您可以将"permutation"或"impurity"传递给参数importance。可以在此处找到两个值的说明：https://alexisperrier.com/datascience/2015/08/27/feature-importance-random-forests-gini-accuracy.html

Answer 3

对于＆＃39;游侠＆＃39;你可以用

来称呼重要性

fit$variable.importance

作为旁注，您可以使用str（）

查看模型的所有可用输出

str(fit)

游侠的重要性

3 个答案: