MLR random forest multi label get feature importance
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$learner, props = "featimp") :
Learner 'multilabel.randomForestSRC' must support properties 'featimp', but does not support featimp'
Solution
You can use extract the variable importance using randomForestSRC::vimp, using the example from here:
library(mlr)
yeast = getTaskData(yeast.task)
labels = colnames(yeast)[1:14]
yeast.task = makeMultilabelTask(id = "multi", data = yeast, target = labels)
lrn.rfsrc = makeLearner("multilabel.randomForestSRC")
mod2 = train(lrn.rfsrc, yeast.task)
vi =randomForestSRC::vimp(mod2$learner.model)
plot(vi,m.target ="label2")
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