ЕТ
Size: a a a
ЕТ
SM
ЕТ
ЕТ
SM
DN
### Train, validate, test
set.seed(145)
sample = sample.split(train_data, SplitRatio = 0.60)
target1_train =subset(train_data,sample ==TRUE)
target1_val_test =subset(train_data,sample ==FALSE)
sample2 = sample.split(target1_val_test, SplitRatio = 0.50)
target1_val =subset(target1_val_test,sample ==TRUE)
target1_test =subset(target1_val_test,sample ==FALSE)
ЕТ
### Train, validate, test
set.seed(145)
sample = sample.split(train_data, SplitRatio = 0.60)
target1_train =subset(train_data,sample ==TRUE)
target1_val_test =subset(train_data,sample ==FALSE)
sample2 = sample.split(target1_val_test, SplitRatio = 0.50)
target1_val =subset(target1_val_test,sample ==TRUE)
target1_test =subset(target1_val_test,sample ==FALSE)
PU
ЕТ
DN
PU
PU
DN
A
А[
PU
> library(data.table)
> my_dt <- data.table(
+ var1 = sample(c('a', 'b'), 2000, replace = TRUE),
+ var2 = rnorm(2000)
+ )
>
> my_dt[, tg := sample(c('train', 'test', 'validate'), .N, TRUE, c(.7, .15, .15))]
> my_dt[, .N / my_dt[, .N], by = tg]
tg V1
1: train 0.6975
2: validate 0.1610
3: test 0.1415
ЕТ
SM
PU