embed (version 0.1.2)

step_woe: Weight of evidence transformation

Description

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.

Usage

step_woe(
  recipe,
  ...,
  role = "predictor",
  outcome,
  trained = FALSE,
  dictionary = NULL,
  Laplace = 1e-06,
  prefix = "woe",
  skip = FALSE,
  id = rand_id("woe")
)
# S3 method for step_woe
tidy(x, ...)

Arguments

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 selections() for more details. For the tidy method, these are not currently used.

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 vars().

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 dictionary(). If `NULL`` the function will build a dictionary with those variables passed to .... See dictionary() for details.

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 1e-6. 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 recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations

id

A character string that is unique to this step to identify it.

x

A step_woe object.

Value

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.

Details

WoE is a transformation of a group of variables that produces a new set of features. The formula is

$$woe_c = log((P(X = c|Y = 1))/(P(X = c|Y = 0)))$$

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().

References

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.249-270.

Examples

# NOT RUN {
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

# }