【发布时间】:2020-07-04 22:52:25
【问题描述】:
由于我对 XGBoost 非常陌生,我正在尝试使用 mlr 库和模型调整参数,但是在使用 setHayperPars() 学习后使用 train() 会引发错误(特别是当我运行 xgmodel 行):colnames(x) 中的错误:缺少参数“x”,没有默认值,我无法识别此错误的含义,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)
谁能告诉我怎么了!?
【问题讨论】:
标签: r machine-learning xgboost r-caret mlr