Nothing
Code
embed:::woe_table(rep(c(0, 1), 20), rep(letters[1:4], 5))
Condition
Error in `embed:::woe_table()`:
! 'outcome' must have exactly 2 categories (has 4)
Code
embed:::woe_table(rep(letters[1:3], 10), rep(c(0, 1, 2), 10))
Condition
Error in `embed:::woe_table()`:
! 'outcome' must have exactly 2 categories (has 3)
Code
embed:::woe_table(rep(letters[1:3], 10), rep(c(0), 30))
Condition
Error in `embed:::woe_table()`:
! 'outcome' must have exactly 2 categories (has 1)
Code
embed:::woe_table(df$x2, df$x1)
Condition
Error in `embed:::woe_table()`:
! 'outcome' must have exactly 2 categories (has 3)
Code
dictionary(df %>% filter(y %in% "B"), "y")
Condition
Error in `dictionary()`:
! 'outcome' must have exactly 2 categories (has 1)
Code
add_woe(df, outcome = "y", x1, dictionary = iris)
Condition
Error in `add_woe()`:
! column "variable" is missing in dictionary.
Code
add_woe(df, outcome = "y", x1, dictionary = iris %>% mutate(variable = 1))
Condition
Error in `add_woe()`:
! column "predictor" is missing in dictionary.
Code
woe_models <- prep(rec, training = credit_tr)
Condition
Warning:
Some columns used by `step_woe()` have categories with less than 10 values: 'Home', 'Job'
Code
prep(rec_all_nominal, training = credit_tr, verbose = TRUE)
Output
oper 1 step woe [training]
Condition
Warning:
Some columns used by `step_woe()` have categories with less than 10 values: 'Home', 'Job'
Output
The retained training set is ~ 0.14 Mb in memory.
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 13
-- Training information
Training data contained 2000 data points and 186 incomplete rows.
-- Operations
* WoE version against outcome Status for: Home and Marital, ... | Trained
Code
prep(rec_all_numeric, training = credit_tr)
Condition
Error in `step_woe()`:
Caused by error in `prep()`:
x All columns selected for the step should be string, factor, or ordered.
* 9 integer variables found: `Seniority`, `Time`, `Age`, ...
Code
recipe(Species ~ ., data = iris3) %>% step_woe(group, outcome = vars(Species)) %>%
prep()
Condition
Error in `step_woe()`:
Caused by error in `dictionary()`:
! 'outcome' must have exactly 2 categories (has 3)
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Operations
* WoE version against outcome mpg for: <none>
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Training information
Training data contained 32 data points and no incomplete rows.
-- Operations
Condition
Warning:
Unknown or uninitialised column: `variable`.
Message
* WoE version against outcome mpg for: <none> | Trained
Code
rec <- prep(rec)
Condition
Warning:
`keep_original_cols` was added to `step_woe()` after this recipe was created.
i Regenerate your recipe to avoid this warning.
Code
print(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 13
-- Operations
* WoE version against outcome Status for: Job and Home
Code
prep(rec)
Condition
Warning:
Some columns used by `step_woe()` have categories with less than 10 values: 'Home', 'Job'
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 13
-- Training information
Training data contained 4454 data points and 415 incomplete rows.
-- Operations
* WoE version against outcome Status for: Job and Home | Trained
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.