在放入决策树模型以预测结果之前,我在R中通过库(食谱)规范化了数字变量。现在,我有了决策树,年龄是节点中重要的变量之一,像>1.5和<1.5. 我想把-1.5转换回一个非标准化的值,以便能够给它一个实际的意义(如年龄>50或
library(recipes)
recipe_obj <- dataset %>%
recipe(formula = anyaki ~.) %>% #specify formula
step_center(all_numeric()) %>% #center data (0 mean)
step_scale(all_numeric()) %>% #std = 1
prep(data = dataset)
dataset_scaled <- bake(recipe_obj, new_data = dataset)
Age是r中recipes包中规范化的变量之一。现在,我正在努力将最终模型中的规范化数据转换回非规范化值,以便能够赋予其实际意义。我该怎么做呢?
您可以使用菜谱和菜谱步骤的tidy()
方法访问这些估计值。点击这里和这里查看更多细节。
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
data(penguins)
penguin_rec <- recipe(~ ., data = penguins) %>%
step_other(all_nominal(), threshold = 0.2, other = "another") %>%
step_normalize(all_numeric()) %>%
step_dummy(all_nominal())
tidy(penguin_rec)
#> # A tibble: 3 × 6
#> number operation type trained skip id
#> <int> <chr> <chr> <lgl> <lgl> <chr>
#> 1 1 step other FALSE FALSE other_ZNJ2R
#> 2 2 step normalize FALSE FALSE normalize_ogEvZ
#> 3 3 step dummy FALSE FALSE dummy_YVCBo
tidy(penguin_rec, number = 1)
#> # A tibble: 1 × 3
#> terms retained id
#> <chr> <chr> <chr>
#> 1 all_nominal() <NA> other_ZNJ2R
penguin_prepped <- prep(penguin_rec, training = penguins)
#> Warning: There are new levels in a factor: NA
tidy(penguin_prepped)
#> # A tibble: 3 × 6
#> number operation type trained skip id
#> <int> <chr> <chr> <lgl> <lgl> <chr>
#> 1 1 step other TRUE FALSE other_ZNJ2R
#> 2 2 step normalize TRUE FALSE normalize_ogEvZ
#> 3 3 step dummy TRUE FALSE dummy_YVCBo
tidy(penguin_prepped, number = 1)
#> # A tibble: 6 × 3
#> terms retained id
#> <chr> <chr> <chr>
#> 1 species Adelie other_ZNJ2R
#> 2 species Gentoo other_ZNJ2R
#> 3 island Biscoe other_ZNJ2R
#> 4 island Dream other_ZNJ2R
#> 5 sex female other_ZNJ2R
#> 6 sex male other_ZNJ2R
tidy(penguin_prepped, number = 2)
#> # A tibble: 8 × 4
#> terms statistic value id
#> <chr> <chr> <dbl> <chr>
#> 1 bill_length_mm mean 43.9 normalize_ogEvZ
#> 2 bill_depth_mm mean 17.2 normalize_ogEvZ
#> 3 flipper_length_mm mean 201. normalize_ogEvZ
#> 4 body_mass_g mean 4202. normalize_ogEvZ
#> 5 bill_length_mm sd 5.46 normalize_ogEvZ
#> 6 bill_depth_mm sd 1.97 normalize_ogEvZ
#> 7 flipper_length_mm sd 14.1 normalize_ogEvZ
#> 8 body_mass_g sd 802. normalize_ogEvZ
由reprex包(v2.0.0)在2021-08-07创建