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我一直在mlr用 Titanic数据集探索这个奇妙的包。我的问题是实现一个随机森林。更具体地说，我想调整cutoff（即，将不纯的叶子分配给给定类的阈值）。问题是该cutoff参数采用两个值，但是，我只能找出超参数mlr为单个值打开。

编码：

library(mlr)
library(dplyr)

dTrain <- read.csv('path/to/data/')

#Defining the Task
trainTask <- makeClassifTask(data = dTrain %>% 
                           select(-Name, -Ticket, -Cabin) %>% 
                           filter(complete.cases(.)), 
                         target = "Survived", 
                         id = "PassengerId")

#Defining Learning
rfLRN <- makeLearner("classif.randomForest")

#Defining the Parameter Space
ps <- makeParamSet(
 makeDiscreteParam("cutoff", values = list(c(.5,.5), c(.75,.25)))
)

这是问题所在，cutoff需要两个值，但是，我不确定如何传递这两个值。上述尝试是错误的。我已经尝试了其他几个参数 makeDiscreteVectorParam生成器，即等....但无济于事。有小费吗？

相反，如果我尝试调整一个参数，例如mtry（即在给定拆分处选择的特征数量），一切正常。

#Defining the Hyperparameter Space
ps = makeParamSet(
  makeDiscreteParam("mtry", values = c(2,3,4,5))
)

#Defining Resampling
cvTask <- makeResampleDesc("CV", iters=5L)

#Defining Search
search <-  makeTuneControlGrid()

#Tune!
tune <- tuneParams(learner = rfLRN
                 ,task = trainTask
                 ,resampling = cvTask
                 ,measures = list(acc)
                 ,par.set = ps
                 ,control = search
                 ,show.info = TRUE)
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1 回答 1

2

看起来您需要为这些分类截止值分配名称，例如：

#Defining the Parameter Space
ps <- makeParamSet(
  makeDiscreteParam("cutoff", values = list(
    a=c(.50,.50),
    b=c(.75,.25)))
)

输出：

> tune <- tuneParams(learner = rfLRN
+                    ,task = trainTask
+                    ,resampling = cvTask
+                    ,measures = list(acc)
+                    ,par.set = ps
+                    ,control = search
+                    ,show.info = TRUE)
[Tune] Started tuning learner classif.randomForest for parameter set:
           Type len Def Constr Req Tunable Trafo
cutoff discrete   -   -    a,b   -    TRUE     -
With control class: TuneControlGrid
Imputation value: -0
[Tune-x] 1: cutoff=a
[Tune-y] 1: acc.test.mean=0.828; time: 0.0 min
[Tune-x] 2: cutoff=b
[Tune-y] 2: acc.test.mean=0.776; time: 0.0 min
[Tune] Result: cutoff=a : acc.test.mean=0.828
于 2017-03-18T01:17:52.023 回答