mlr程序包r:功能选择顺序正向搜索错误:必须至少有1列
我试图使用R中的mlr包,使用顺序向前搜索,将特征选择应用于一个打包的学习者mlr程序包r:功能选择顺序正向搜索错误:必须至少有1列,r,feature-selection,mlr,R,Feature Selection,Mlr,我试图使用R中的mlr包,使用顺序向前搜索,将特征选择应用于一个打包的学习者 d <- data.frame(a = rnorm(1000, mean = 1), b = rnorm(1000, mean = 2), c = rnorm(1000, mean = 3), target = as.factor(rbinom(1000, 1, prob = 0.5))) t
d <- data.frame(a = rnorm(1000, mean = 1),
b = rnorm(1000, mean = 2),
c = rnorm(1000, mean = 3),
target = as.factor(rbinom(1000, 1, prob = 0.5)))
t <- makeClassifTask(data = d,
target = 'target',
positive = '1')
logreg.lrn <- makeLearner('classif.logreg')
logreg_bagged.lrn <- makeBaggingWrapper(logreg.lrn)
cntrl.sfs <- makeFeatSelControlSequential(method = "sfs",
alpha = 0.01,
max.features = 10,
maxit = 3)
logreg_bagged_featsel.lrn <- makeFeatSelWrapper(logreg_bagged.lrn,
resampling = makeResampleDesc('CV',
iters = 3),
measures = mmce,
control = cntrl.sfs)
mlr::train(logreg_bagged_featsel.lrn, classif.task)
如果改用顺序反向搜索,则不会出现错误:
cntrl.sbs <- makeFeatSelControlSequential(method = "sbs",
alpha = 0.01,
max.features = 10,
maxit = 3)
logreg_bagged_featsel.lrn <- makeFeatSelWrapper(logreg_bagged.lrn,
resampling = makeResampleDesc('CV',
iters = 3),
measures = mmce,
control = cntrl.sbs)
mlr::train(logreg_bagged_featsel.lrn, classif.task)
[FeatSel] Started selecting features for learner 'classif.logreg.bagged'
With control class: FeatSelControlSequential
Imputation value: 1
[FeatSel-x] 1: 111 (3 bits)
[FeatSel-y] 1: mmce.test.mean=0.447; time: 0.0 min
[FeatSel-x] 2: 011 (2 bits)
[FeatSel-y] 2: mmce.test.mean=0.509; time: 0.0 min
[FeatSel-x] 2: 101 (2 bits)
[FeatSel-y] 2: mmce.test.mean=0.448; time: 0.0 min
[FeatSel-x] 2: 110 (2 bits)
[FeatSel-y] 2: mmce.test.mean=0.456; time: 0.0 min
[FeatSel-x] 3: 001 (1 bits)
[FeatSel-y] 3: mmce.test.mean=0.51; time: 0.0 min
[FeatSel-x] 3: 100 (1 bits)
[FeatSel-y] 3: mmce.test.mean=0.468; time: 0.0 min
[FeatSel] Result: ac (2 bits)
Model for learner.id=classif.logreg.bagged.featsel; learner.class=FeatSelWrapper
Trained on: task.id = classif.df; obs = 1000; features = 3
Hyperparameters: model=FALSE
cntrl.sbs顺序前向搜索从空模型开始,即没有特征。装袋包装器不支持此操作。我为这件事开了个玩笑
cntrl.sbs <- makeFeatSelControlSequential(method = "sbs",
alpha = 0.01,
max.features = 10,
maxit = 3)
logreg_bagged_featsel.lrn <- makeFeatSelWrapper(logreg_bagged.lrn,
resampling = makeResampleDesc('CV',
iters = 3),
measures = mmce,
control = cntrl.sbs)
mlr::train(logreg_bagged_featsel.lrn, classif.task)
[FeatSel] Started selecting features for learner 'classif.logreg.bagged'
With control class: FeatSelControlSequential
Imputation value: 1
[FeatSel-x] 1: 111 (3 bits)
[FeatSel-y] 1: mmce.test.mean=0.447; time: 0.0 min
[FeatSel-x] 2: 011 (2 bits)
[FeatSel-y] 2: mmce.test.mean=0.509; time: 0.0 min
[FeatSel-x] 2: 101 (2 bits)
[FeatSel-y] 2: mmce.test.mean=0.448; time: 0.0 min
[FeatSel-x] 2: 110 (2 bits)
[FeatSel-y] 2: mmce.test.mean=0.456; time: 0.0 min
[FeatSel-x] 3: 001 (1 bits)
[FeatSel-y] 3: mmce.test.mean=0.51; time: 0.0 min
[FeatSel-x] 3: 100 (1 bits)
[FeatSel-y] 3: mmce.test.mean=0.468; time: 0.0 min
[FeatSel] Result: ac (2 bits)
Model for learner.id=classif.logreg.bagged.featsel; learner.class=FeatSelWrapper
Trained on: task.id = classif.df; obs = 1000; features = 3
Hyperparameters: model=FALSE