Question

我试图使用MSE在MLR中拆分我的决策树。这是我的代码

Error in setHyperPars2.Learner(learner, insert(par.vals, args)) : 
  classif.rpart: Setting parameter split without available description object!
Did you mean one of these hyperparameters instead: minsplit cp xval
You can switch off this check by using configureMlr!

它给了我错误

rpart

我知道如何在MLR上执行此操作。但是<html:form action="action1"> <html:hidden property="prop1" name="form1" value="val1" /> <html:hidden property="prop2" name="form1" value="val2" /> <html:text property="prop3" name="form1" /> </html>

没有意识形态

Answer 1

split参数在rpart(..., parms = list(split = "mse"))下的列表中传递。因此可以在mlr中设置如下：

library(mlr)
cl = "classif.rpart"
learner = makeLearner(cl = cl, predict.type = "prob", par.vals = list(parms = list(split="mse")), fix.factors.prediction = TRUE)
m = train(learner, iris.task)

在结果中我们可以看到它已正确传递

m$learner.model$call
# rpart::rpart(formula = f, data = d, parms = list(split = "mse"), xval = 0L)

使用MSE在MLR上拆分决策树

1 个答案: