我想使用OneR包中的breastcancer数据集创建一个正则化的逻辑回归模型来预测Class。我希望将这一切整合到tidymodels框架下的一个整洁的工作流程中。

library(tidymodels)library(OneR)#指定模型bc.lr = logistic_reg(  mode="classification",  penalty = tune(),  mixture=1) %>%  set_engine("glmnet")#使用4折交叉验证调整惩罚项cv_splits<-vfold_cv(breastcancer,v=4,strata="Class")#简单配方，用于缩放所有预测变量并删除包含NA的观测值bc.recipe <- recipe (Class ~., data = breastcancer) %>%  step_normalize(all_predictors()) %>%  step_naomit(all_predictors(), all_outcomes()) %>%  prep()#设置调参网格tuning_grid = grid_regular(penalty(range = c(0, 0.5)),                           levels = 10,                           original = F)#将所有内容整合到一个工作流程中bc.wkfl <- workflow() %>%  add_recipe(bc.recipe) %>%  add_model(bc.lr)#模型拟合tune = tune_grid(bc.wkfl,                 resample = cv_splits,                 grid = tuning_grid,                 metrics = metric_set(accuracy),                 control = control_grid(save_pred = T))

当我尝试调用tune_grid时，得到一个奇怪的错误。

Fold1: model 1/1 (predictions): Error: Column `.row` must be length ....

回答：

这里的问题在于配方步骤对NA值的处理。这是一个你需要仔细考虑“skipping”的步骤。来自那篇文章的引用：

在进行重抽样或训练/测试分割时，某些操作对于用于建模的数据是有意义的，但对于新样本或测试集来说可能存在问题。

library(tidymodels)#> ── Attaching packages ────────────────────────────────────────── tidymodels 0.1.0 ──#> ✓ broom     0.5.6      ✓ recipes   0.1.12#> ✓ dials     0.0.6      ✓ rsample   0.0.6 #> ✓ dplyr     0.8.5      ✓ tibble    3.0.1 #> ✓ ggplot2   3.3.0      ✓ tune      0.1.0 #> ✓ infer     0.5.1      ✓ workflows 0.1.1 #> ✓ parsnip   0.1.1      ✓ yardstick 0.0.6 #> ✓ purrr     0.3.4#> ── Conflicts ───────────────────────────────────────────── tidymodels_conflicts() ──#> x purrr::discard()  masks scales::discard()#> x dplyr::filter()   masks stats::filter()#> x dplyr::lag()      masks stats::lag()#> x ggplot2::margin() masks dials::margin()#> x recipes::step()   masks stats::step()library(OneR)lasso_spec <- logistic_reg(penalty = tune(), mixture = 1) %>%  set_engine("glmnet")## 交叉验证分割cancer_splits <- vfold_cv(breastcancer, v = 4, strata = Class)## 预处理配方（注意设置skip = TRUE）cancer_rec <- recipe(Class ~ ., data = breastcancer) %>%  step_naomit(all_predictors(), skip = TRUE) %>%  step_normalize(all_predictors())## 调参网格tuning_grid <- grid_regular(penalty(),                            levels = 10)## 将所有内容整合到一个工作流程中cancer_wf <- workflow() %>%  add_recipe(cancer_rec) %>%  add_model(lasso_spec)## 拟合cancer_res <- tune_grid(  cancer_wf,  resamples = cancer_splits,  grid = tuning_grid,  control = control_grid(save_pred = TRUE))cancer_res#> #  4-fold cross-validation using stratification #> # A tibble: 4 x 5#>   splits            id    .metrics          .notes           .predictions       #>   <list>            <chr> <list>            <list>           <list>             #> 1 <split [523/176]> Fold1 <tibble [20 × 4]> <tibble [0 × 1]> <tibble [1,760 × 6…#> 2 <split [524/175]> Fold2 <tibble [20 × 4]> <tibble [0 × 1]> <tibble [1,750 × 6…#> 3 <split [525/174]> Fold3 <tibble [20 × 4]> <tibble [0 × 1]> <tibble [1,740 × 6…#> 4 <split [525/174]> Fold4 <tibble [20 × 4]> <tibble [0 × 1]> <tibble [1,740 × 6…

^{Created on 2020-05-14 by the reprex package (v0.3.0)}

请注意，设置skip = TRUE允许你以适当的方式处理新数据中的NA值。