Description Usage Arguments Details Value References Examples
step_woe
creates a specification of a
recipe step that will transform nominal data into its numerical
transformation based on weights of evidence against a binary outcome.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
recipe 
A recipe object. The step will be added to the sequence of operations for this recipe. 
... 
One or more selector functions to choose which
variables will be used to compute the components. See

role 
For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new woe components columns created by the original variables will be used as predictors in a model. 
outcome 
The bare name of the binary outcome encased in 
trained 
A logical to indicate if the quantities for preprocessing have been estimated. 
dictionary 
A tbl. A map of levels and woe values. It must
have the same layout than the output returned from 
Laplace 
The Laplace smoothing parameter. A value usually applied to avoid Inf/Inf from predictor category with only one outcome class. Set to 0 to allow Inf/Inf. The default is 1e6. Also known as 'pseudocount' parameter of the Laplace smoothing technique. 
prefix 
A character string that will be the prefix to the resulting new variables. See notes below. 
skip 
A logical. Should the step be skipped when the
recipe is baked by 
id 
A character string that is unique to this step to identify it. 
x 
A 
WoE is a transformation of a group of variables that produces a new set of features. The formula is
woe_c = log((P(X = cY = 1))/(P(X = cY = 0)))
where c goes from 1 to C levels of a given nominal predictor variable X.
These components are designed to transform nominal variables into
numerical ones with the property that the order and magnitude
reflects the association with a binary outcome. To apply it on
numerical predictors, it is advisable to discretize the variables
prior to running WoE. Here, each variable will be binarized to
have woe associated later. This can achieved by using step_discretize()
.
The argument Laplace
is an small quantity added to the
proportions of 1's and 0's with the goal to avoid log(p/0) or
log(0/p) results. The numerical woe versions will have names that
begin with woe_
followed by the respective original name of the
variables. See Good (1985).
One can pass a custom dictionary
tibble to step_woe()
.
It must have the same structure of the output from
dictionary()
(see examples). If not provided it will be
created automatically. The role of this tibble is to store the map
between the levels of nominal predictor to its woe values. You may
want to tweak this object with the goal to fix the orders between
the levels of one given predictor. One easy way to do this is by
tweaking an output returned from dictionary()
.
An updated version of recipe
with the new step
added to the sequence of existing steps (if any). For the
tidy
method, a tibble with the woe dictionary used to map
categories with woe values.
Kullback, S. (1959). Information Theory and Statistics. Wiley, New York.
Hastie, T., Tibshirani, R. and Friedman, J. (1986). Elements of Statistical Learning, Second Edition, Springer, 2009.
Good, I. J. (1985), "Weight of evidence: A brief survey", Bayesian Statistics, 2, pp.249270.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  library(modeldata)
data("credit_data")
set.seed(111)
in_training < sample(1:nrow(credit_data), 2000)
credit_tr < credit_data[ in_training, ]
credit_te < credit_data[in_training, ]
rec < recipe(Status ~ ., data = credit_tr) %>%
step_woe(Job, Home, outcome = vars(Status))
woe_models < prep(rec, training = credit_tr)
# the encoding:
bake(woe_models, new_data = credit_te %>% slice(1:5), starts_with("woe"))
# the original data
credit_te %>% slice(1:5) %>% dplyr::select(Job, Home)
# the details:
tidy(woe_models, number = 1)
# Example of custom dictionary + tweaking
# custom dictionary
woe_dict_custom < credit_tr %>% dictionary(Job, Home, outcome = "Status")
woe_dict_custom[4, "woe"] < 1.23 #tweak
#passing custom dict to step_woe()
rec_custom < recipe(Status ~ ., data = credit_tr) %>%
step_woe(Job, Home, outcome = vars(Status), dictionary = woe_dict_custom) %>%
prep
rec_custom_baked < bake(rec_custom, new_data = credit_te)
rec_custom_baked %>% dplyr::filter(woe_Job == 1.23) %>% head

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