MLR random forest multi label get feature importance

问题

I am using multilabel.randomForestSRC learner from mlr package for a multi-label classification problem I would like to return the variables importances

The getFeatureImportance function return this issue :

code:

getFeatureImportance(mod)

Error:

Error in checkLearner(object$learner, props = "featimp") : 
Learner 'multilabel.randomForestSRC' must support properties 'featimp', but does not support featimp'

回答1:

You can use extract the variable importance using randomForestSRC::vimp, using the example from here:

library(mlr)
yeast = getTaskData(yeast.task)
labels = colnames(yeast)[1:14]
yeast.task = makeMultilabelTask(id = "multi", data = yeast, target = labels)
lrn.rfsrc = makeLearner("multilabel.randomForestSRC")
mod2 = train(lrn.rfsrc, yeast.task)

vi =randomForestSRC::vimp(mod2$learner.model)
plot(vi,m.target ="label2")

来源：https://stackoverflow.com/questions/61754110/mlr-random-forest-multi-label-get-feature-importance