警告消息：所有模型在[fit_resamples（）]中失败。请参阅“.notes”列
我在警告消息：所有模型在[fit_resamples（）]中失败。请参阅“.notes”列,r,logistic-regression,glm,tidymodels,r-recipes,R,Logistic Regression,Glm,Tidymodels,R Recipes,我在tidymodels包中使用了recipe（）函数来插补缺失值和修复不平衡数据 这是我的数据 mer_df <- mer2 %>% filter(!is.na(laststagestatus2)) %>% select(Id, Age_Range__c, Gender__c, numberoflead, leadduration, firsttouch, lasttouch, laststagestatus2)%>% mutate_if(is.chara
tidymodelsrecipe（）mer_df <- mer2 %>%
filter(!is.na(laststagestatus2)) %>%
select(Id, Age_Range__c, Gender__c, numberoflead, leadduration, firsttouch, lasttouch, laststagestatus2)%>%
mutate_if(is.character, factor) %>%
mutate_if(is.logical, as.integer)
# A tibble: 197,836 x 8
Id Age_Range__c Gender__c numberoflead leadduration firsttouch lasttouch
<fct> <fct> <fct> <int> <dbl> <fct> <fct>
1 0010~ NA NA 2 5.99 Dealer IB~ Walk in
2 0010~ NA NA 1 0 Online Se~ Online S~
3 0010~ NA NA 1 0 Walk in Walk in
4 0010~ NA NA 1 0 Online Se~ Online S~
5 0010~ NA NA 2 0.0128 Dealer IB~ Dealer I~
6 0010~ NA NA 1 0 OB Call OB Call
7 0010~ NA NA 1 0 Dealer IB~ Dealer I~
8 0010~ NA NA 4 73.9 Dealer IB~ Walk in
9 0010~ NA Male 24 0.000208 OB Call OB Call
10 0010~ NA NA 18 0.000150 OB Call OB Call
# ... with 197,826 more rows, and 1 more variable: laststagestatus2 <fct>
Warning message:
All models failed in [fit_resamples()]. See the `.notes` column.
Resampling results
10-fold cross-validation using stratification
A tibble: 10 x 5
splits id .metrics .notes .predictions
<list> <chr> <list> <list> <list>
1 <split [133.5K/14.8K]> Fold01 <NULL> <tibble [1 x 1]> <NULL>
2 <split [133.5K/14.8K]> Fold02 <NULL> <tibble [1 x 1]> <NULL>
3 <split [133.5K/14.8K]> Fold03 <NULL> <tibble [1 x 1]> <NULL>
4 <split [133.5K/14.8K]> Fold04 <NULL> <tibble [1 x 1]> <NULL>
5 <split [133.5K/14.8K]> Fold05 <NULL> <tibble [1 x 1]> <NULL>
6 <split [133.5K/14.8K]> Fold06 <NULL> <tibble [1 x 1]> <NULL>
7 <split [133.5K/14.8K]> Fold07 <NULL> <tibble [1 x 1]> <NULL>
8 <split [133.5K/14.8K]> Fold08 <NULL> <tibble [1 x 1]> <NULL>
9 <split [133.5K/14.8K]> Fold09 <NULL> <tibble [1 x 1]> <NULL>
10 <split [133.5K/14.8K]> Fold10 <NULL> <tibble [1 x 1]> <NULL>
Warning message:
This tuning result has notes. Example notes on model fitting include:
recipe: Error: could not find function "all_nominal"
警告消息：
所有模型在[fit_resamples（）]中失败。请参阅“.notes”列。
重采样结果
使用分层的10倍交叉验证
一个tibble:10x5
拆分id.度量.注释.预测
1折叠01
2折叠02
3折叠03
4折叠4
5.折叠
折叠
7折叠07
8折叠08
9折叠09
10折叠10
警告信息：
此调整结果具有注释。有关模型拟合的示例注释包括：
配方：错误：找不到函数“all_nominal”
有人对如何做到这一点有什么建议吗？非常感谢你的帮助 如果你能提供一个完整的可复制的例子，那就更好了。我强烈怀疑你是在Windows机器上工作的。当前有一个bug影响了tidymodels在Windows上的并行处理。到今天为止，配方已经在CRAN上更新，但是您需要在GitHub上更新到tune和parsnip的开发版本，以便在workers中正确加载包。我们正在尽快将这些修复程序安装到CRAN上。
doParallel::registerDoParallel()
glm_rs <- mer_wf %>%
add_model(glm_spec) %>%
fit_resamples(
resamples = mer_folds,
metrics = mer_metrics,
control = control_resamples(save_pred = TRUE)
glm_rs
Warning message:
All models failed in [fit_resamples()]. See the `.notes` column.
Resampling results
10-fold cross-validation using stratification
A tibble: 10 x 5
splits id .metrics .notes .predictions
<list> <chr> <list> <list> <list>
1 <split [133.5K/14.8K]> Fold01 <NULL> <tibble [1 x 1]> <NULL>
2 <split [133.5K/14.8K]> Fold02 <NULL> <tibble [1 x 1]> <NULL>
3 <split [133.5K/14.8K]> Fold03 <NULL> <tibble [1 x 1]> <NULL>
4 <split [133.5K/14.8K]> Fold04 <NULL> <tibble [1 x 1]> <NULL>
5 <split [133.5K/14.8K]> Fold05 <NULL> <tibble [1 x 1]> <NULL>
6 <split [133.5K/14.8K]> Fold06 <NULL> <tibble [1 x 1]> <NULL>
7 <split [133.5K/14.8K]> Fold07 <NULL> <tibble [1 x 1]> <NULL>
8 <split [133.5K/14.8K]> Fold08 <NULL> <tibble [1 x 1]> <NULL>
9 <split [133.5K/14.8K]> Fold09 <NULL> <tibble [1 x 1]> <NULL>
10 <split [133.5K/14.8K]> Fold10 <NULL> <tibble [1 x 1]> <NULL>
Warning message:
This tuning result has notes. Example notes on model fitting include:
recipe: Error: could not find function "all_nominal"