这与使用 step_naomit 的 Predict 和使用 tidymodels 保留 ID的问题相同,但即使有一个可接受的答案,OP 的最后一条评论指出了“id 变量”被用作预测器的问题,正如在查看时可以看到的那样model$fit$variable.importance
.
我有一个我想保留的带有“id variables”的数据集。我想我可以通过 recipe() 规范来实现这一点。
library(tidymodels)
# label is an identifier variable I want to keep even though it's not
# a predictor
df <- tibble(label = 1:50,
x = rnorm(50, 0, 5),
f = factor(sample(c('a', 'b', 'c'), 50, replace = TRUE)),
y = factor(sample(c('Y', 'N'), 50, replace = TRUE)) )
df_split <- initial_split(df, prop = 0.70)
# Make up any recipe: just note I specify 'label' as "id variable"
rec <- recipe(training(df_split)) %>%
update_role(label, new_role = "id variable") %>%
update_role(y, new_role = "outcome") %>%
update_role(x, new_role = "predictor") %>%
update_role(f, new_role = "predictor") %>%
step_corr(all_numeric(), -all_outcomes()) %>%
step_dummy(all_predictors(),-all_numeric()) %>%
step_meanimpute(all_numeric(), -all_outcomes())
train_juiced <- prep(rec, training(df_split)) %>% juice()
logit_fit <- logistic_reg(mode = "classification") %>%
set_engine(engine = "glm") %>%
fit(y ~ ., data = train_juiced)
# Why is label a variable in the model ?
logit_fit[['fit']][['coefficients']]
#> (Intercept) label x f_b f_c
#> 1.03664140 -0.01405316 0.22357266 -1.80701531 -1.66285399
由reprex 包(v0.3.0)于 2020 年 1 月 27 日创建
但即使我确实指定label
了一个 id 变量,它也被用作预测变量。所以也许我可以在公式中使用我想要的特定术语,并专门添加label
为 id 变量。
rec <- recipe(training(df_split), y ~ x + f) %>%
update_role(label, new_role = "id variable") %>%
step_corr(all_numeric(), -all_outcomes()) %>%
step_dummy(all_predictors(),-all_numeric()) %>%
step_meanimpute(all_numeric(), -all_outcomes())
#> Error in .f(.x[[i]], ...): object 'label' not found
由reprex 包(v0.3.0)于 2020 年 1 月 27 日创建
我可以试着不提label
rec <- recipe(training(df_split), y ~ x + f) %>%
step_corr(all_numeric(), -all_outcomes()) %>%
step_dummy(all_predictors(),-all_numeric()) %>%
step_meanimpute(all_numeric(), -all_outcomes())
train_juiced <- prep(rec, training(df_split)) %>% juice()
logit_fit <- logistic_reg(mode = "classification") %>%
set_engine(engine = "glm") %>%
fit(y ~ ., data = train_juiced)
# Why is label a variable in the model ?
logit_fit[['fit']][['coefficients']]
#> (Intercept) x f_b f_c
#> -0.98950228 0.03734093 0.98945339 1.27014824
train_juiced
#> # A tibble: 35 x 4
#> x y f_b f_c
#> <dbl> <fct> <dbl> <dbl>
#> 1 -0.928 Y 1 0
#> 2 4.54 N 0 0
#> 3 -1.14 N 1 0
#> 4 -5.19 N 1 0
#> 5 -4.79 N 0 0
#> 6 -6.00 N 0 0
#> 7 3.83 N 0 1
#> 8 -8.66 Y 1 0
#> 9 -0.0849 Y 1 0
#> 10 -3.57 Y 0 1
#> # ... with 25 more rows
由reprex 包(v0.3.0)于 2020 年 1 月 27 日创建
好的,所以模型有效,但我的label
.
我该怎么做?