在MLR中，如何仅将逻辑超参数设置为TRUE或FALSE？
我使用这个数据集尝试使用classif.ada进行分类任务在MLR中，如何仅将逻辑超参数设置为TRUE或FALSE？,r,hyperparameters,mlr,R,Hyperparameters,Mlr,我使用这个数据集尝试使用classif.ada进行分类任务 library(mlr) data("HouseVotes84") #Using HouseVotes84 as Classification Task Dataset and mtcars as Regression Task Dataset dummy_data_classif <- HouseVotes84[,2:length(colnames(HouseVotes84))] %>% muta
library(mlr)
data("HouseVotes84")
#Using HouseVotes84 as Classification Task Dataset and mtcars as Regression Task Dataset
dummy_data_classif <- HouseVotes84[,2:length(colnames(HouseVotes84))] %>%
mutate_if(is.factor, as.numeric)
dummy_data_classif <- data.frame(cbind(Class=HouseVotes84[,1], dummy_data_classif))
dummy_data_classif[is.na(dummy_data_classif)] <- 0
dummy_data_classif_numeric <- dummy_data_classif[-1] %>%
mutate_if(is.factor, as.numeric)
dummy_data_classif_numeric <- data.frame(cbind(dummy_data_classif[1],
dummy_data_classif_numeric))
colnames(dummy_data_classif_numeric) <- colnames(dummy_data_classif)
如何使
“model.coef”FALSE参数VAL中设置该参数。看
参数集中指定的参数将始终在指定范围内进行调整
PS：使用为全局变量指定参数集为学习者创建固定参数，在创建学习者时在par.vals
中设置该参数。看
参数集中指定的参数将始终在指定范围内进行调整
PS：使用将参数集指定给GlobalEnv如果希望固定一个值且不包含在搜索空间中，则必须手动设置此参数的值并将其从搜索空间中排除：
hyperparam$pars$model.coef = NULL
learner <- makeLearner("classif.ada", par.vals = list(model.coef = FALSE))
lrn_tune <- tuneParams(learner = learner, task = task,
resampling = resampling_method,
control = hyper_search, par.set = hyperparam)
hyperparam$pars$model.coef=NULL
学习者如果希望一个值固定且不包含在搜索空间中，则必须手动设置此参数的值，并将其从搜索空间中排除：
hyperparam$pars$model.coef = NULL
learner <- makeLearner("classif.ada", par.vals = list(model.coef = FALSE))
lrn_tune <- tuneParams(learner = learner, task = task,
resampling = resampling_method,
control = hyper_search, par.set = hyperparam)
hyperparam$pars$model.coef=NULL
首先，您定义lrn抱歉，该方法实际上是classif.ada，我使用parse，因为字符串paramset的结果来自内置函数：），之后需要它动态生成param sets首先，您定义lrn抱歉，该方法实际上是classif.ada，我使用parse，由于字符串paramset的结果来自一个内置函数：），以后需要它动态生成param sets我先前试图从搜索空间中删除model.coef，但由于默认值为TRUE，如果为TRUE，则会由于某种原因导致调整失败，因此，我可能需要直接更改：）我之前曾尝试从搜索空间中删除model.coef，但由于默认值为TRUE，如果为TRUE，则会因某种原因导致调整失败，因此我可能需要直接更改：）
[Tune] Started tuning learner classif.ada for parameter set:
Type len Def Constr Req Tunable Trafo
iter integer - - 50 to 250 - TRUE -
max.iter integer - - 30 to 200 - TRUE -
model.coef logical - FALSE - - FALSE -
loss discrete - - exponential,logistic - TRUE -
type discrete - - discrete,real,gentle - TRUE -
nu numeric - - 0 to 100 - TRUE -
bag.frac numeric - - 0 to 1 - TRUE -
bag.shift logical - - - - TRUE -
delta numeric - - 0 to 1e-07 - TRUE -
minsplit integer - - 1 to 30 - TRUE -
minbucket integer - - 1 to 20 - TRUE -
cp numeric - - 0 to 1 - TRUE -
maxcompete integer - - 0 to 6 - TRUE -
maxsurrogate integer - - 0 to 7.5 - TRUE -
usesurrogate discrete - - 0,1,2 - TRUE -
surrogatestyle discrete - - 0,1 - TRUE -
maxdepth integer - - 1 to 30 - TRUE -
With control class: TuneControlRandom
Imputation value: 1
[Tune-x] 1: iter=233; max.iter=141; model.coef=FALSE; loss=exponential; type=gentle; nu=63.5; bag.frac=0.686; bag.shift=TRUE; delta=3.49e-08; minsplit=21; minbucket=2; cp=0.881; maxcompete=1; maxsurrogate=2; usesurrogate=2; surrogatestyle=0; maxdepth=17
[Tune-y] 1: mmce.test.mean=0.0598309; time: 0.8 min
[Tune-x] 2: iter=230; max.iter=115; model.coef=TRUE; loss=exponential; type=gentle; nu=39.6; bag.frac=0.35; bag.shift=TRUE; delta=4.87e-08; minsplit=1; minbucket=2; cp=0.523; maxcompete=4; maxsurrogate=7; usesurrogate=0; surrogatestyle=1; maxdepth=21
Error in if (any(wt < 0)) stop("negative weights not allowed") :
missing value where TRUE/FALSE needed
In addition: Warning message:
In log((1 - errm)/errm) :
Error in if (any(wt < 0)) stop("negative weights not allowed") :
missing value where TRUE/FALSE needed
hyperparam$pars$model.coef = NULL
learner <- makeLearner("classif.ada", par.vals = list(model.coef = FALSE))
lrn_tune <- tuneParams(learner = learner, task = task,
resampling = resampling_method,
control = hyper_search, par.set = hyperparam)