作为XGBoost的新手,我试图使用mlr库和模型来调整参数,但是在使用setHayperPars()后,使用train()学习会抛出错误(尤其是当我运行xgmodel时)行):colnames(x)中的错误:参数“ x”丢失,没有默认值,我无法识别此错误是什么意思,下面是代码:
library(mlr)
library(dplyr)
library(caret)
library(xgboost)
set.seed(12345)
n=dim(mydata)[1]
id=sample(1:n, floor(n*0.6))
train=mydata[id,]
test=mydata[-id,]
traintask = makeClassifTask (data = train,target = "label")
testtask = makeClassifTask (data = test,target = "label")
#create learner
lrn = makeLearner("classif.xgboost",
predict.type = "response")
lrn$par.vals = list( objective="multi:softprob",
eval_metric="merror")
#set parameter space
params = makeParamSet( makeIntegerParam("max_depth",lower = 3L,upper = 10L),
makeIntegerParam("nrounds",lower = 20L,upper = 100L),
makeNumericParam("eta",lower = 0.1, upper = 0.3),
makeNumericParam("min_child_weight",lower = 1L,upper = 10L),
makeNumericParam("subsample",lower = 0.5,upper = 1),
makeNumericParam("colsample_bytree",lower = 0.5,upper = 1))
#set resampling strategy
configureMlr(show.learner.output = FALSE, show.info = FALSE)
rdesc = makeResampleDesc("CV",stratify = T,iters=5L)
# set the search optimization strategy
ctrl = makeTuneControlRandom(maxit = 10L)
# parameter tuning
set.seed(12345)
mytune = tuneParams(learner = lrn, task = traintask,
resampling = rdesc, measures = acc,
par.set = params, control = ctrl,
show.info = FALSE)
# build model using the tuned paramters
#set hyperparameters
lrn_tune = setHyperPars(lrn,par.vals = mytune$x)
#train model
xgmodel = train(learner = lrn_tune,task = traintask)
谁能告诉我这是怎么回事!?
您必须非常加载多个可能涉及相同名称的方法的程序包时要小心-这里caret
和mlr
都包含train
方法。此外,library
语句的order很重要:这里,由于caret
在mlr
之后加载,因此它会屏蔽具有相同名称的函数(以及以前加载的所有其他软件包),就像train
。
在您的情况下,您显然想使用train
中的mlr
方法(而不是caret
中的方法,则应在代码中明确声明:
xgmodel = mlr::train(learner = lrn_tune,task = traintask)