 Tidymodels: Cross-Validated Target-Encoding

Hello,

I have a dataset with a categorical variable of 699 levels. I am predicting a binary response. I would like to encode a numeric variable with the mean per category level of the binary outcome. What is the best way to accomplish this? If possible, I would like to cross validate the predictor. Please see the following "attempt" with play data. A vignette or blog about this topic would be helpful!

library(tidyverse)
library(tidymodels)

set.seed(1)
dat<-data.frame(col_a=sample(letters,size = 10000,replace = TRUE),
col_b=sample(letters,size = 10000,replace = TRUE))

dat<-dat %>%
mutate(concat=paste(col_a,col_b,sep="-"))

set.seed(2)
y<-rbinom(n = 10000,size = 1,prob = .2)
dat\$y<-y

concat_mean<-dat %>%
group_by(concat) %>%
summarise(concat_mean=mean(y))

dat<-left_join(dat,concat_mean)
dat\$y<-as.factor(dat\$y)
dat\$imputed_mean<-NA

imputed_dat <-
recipe(y ~ ., data = dat) %>%
step_impute_linear(
imputed_mean,
impute_with = imp_vars(concat)
)

prep(imputed_dat)

You can do this using the embed package. You definitely should not do it prior to resampling; use on the the step_lencode_*() functions do it for you (see example below).

There is a vignette on these methods too.

library(tidymodels)
library(embed)

set.seed(1)
dat <-
data.frame(
col_a = sample(letters, size = 10000, replace = TRUE),
col_b = sample(letters, size = 10000, replace = TRUE),
col_c = rnorm(10000),
y = factor(sample(LETTERS[1:2], 1000, replace = TRUE))
)  %>%
mutate(concat = paste(col_a, col_b, sep = "-"))

rec <-
recipe(y ~ concat + col_c, data = dat) %>%
# See functions named step_lencode_* in the embed package
step_lencode_mixed(concat, outcome = vars(y))

lr_spec <- logistic_reg()

set.seed(2)
resamples <- vfold_cv(dat)

lr_res <-
lr_spec %>%
fit_resamples(rec, resamples = resamples)

collect_metrics(lr_res)
#> # A tibble: 2 × 6
#>   .metric  .estimator  mean     n std_err .config
#>   <chr>    <chr>      <dbl> <int>   <dbl> <chr>
#> 1 accuracy binary     0.516    10 0.00479 Preprocessor1_Model1
#> 2 roc_auc  binary     0.517    10 0.00636 Preprocessor1_Model1

Created on 2022-01-03 by the reprex package (v2.0.0)

Thank you for the illustration. This makes sense and I think I can work from here.

I have a related question: Should I down-sample my training dataset before or after likelihood encoding?

I would say before but you should try it both ways and see if there is a difference

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