游侠的变量重要性

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，none 这些工作：

> 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.

知道我是否以及如何获得它吗？

谢谢。

Answer 1

varImp(fit) 会帮你拿的。

为了解决这个问题，我查看了 names(fit)，这让我找到了 names(fit$modelInfo) - 然后您会看到 varImp 作为选项之一。

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。可以在此处找到这两个值的说明：https://alexisperrier.com/datascience/2015/08/27/feature-importance-random-forests-gini-accuracy.html

Answer 3

对于 'ranger' 包，您可以使用

调用重要性

fit$variable.importance

附带说明一下，您可以使用 str()

查看模型的所有可用输出

str(fit)

游侠的变量重要性

Variable importance with ranger

r

machine-learning

random-forest

r-caret