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.
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, ...)
| 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. |
| 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 1e-6. Also kwon 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 |
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.
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 respecttive 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().
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.
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 = Status) woe_models <- prep(rec, training = credit_tr) # the encoding: bake(woe_models, new_data = credit_te %>% slice(1:5), starts_with("woe"))#> # A tibble: 5 x 2 #> woe_Job woe_Home #> <dbl> <dbl> #> 1 0.451 -0.519 #> 2 -0.187 0.512 #> 3 0.451 0.512 #> 4 -0.187 0.512 #> 5 -1.51 0.0519#> Job Home #> 1 fixed rent #> 2 freelance owner #> 3 fixed owner #> 4 freelance owner #> 5 partime parents#> # A tibble: 12 x 9 #> terms value n_tot n_bad n_good p_bad p_good woe id #> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 Job fixed 1261 273 988 0.451 0.708 0.451 woe_omZtf #> 2 Job freelance 463 159 304 0.263 0.218 -0.187 woe_omZtf #> 3 Job others 74 39 35 0.0645 0.0251 -0.944 woe_omZtf #> 4 Job partime 201 133 68 0.220 0.0487 -1.51 woe_omZtf #> 5 Job NA 1 1 0 0.00165 0 -14.7 woe_omZtf #> 6 Home ignore 8 4 4 0.00661 0.00287 -0.835 woe_omZtf #> 7 Home other 161 78 83 0.129 0.0595 -0.773 woe_omZtf #> 8 Home owner 931 192 739 0.317 0.530 0.512 woe_omZtf #> 9 Home parents 336 98 238 0.162 0.171 0.0519 woe_omZtf #> 10 Home priv 113 42 71 0.0694 0.0509 -0.310 woe_omZtf #> 11 Home rent 446 188 258 0.311 0.185 -0.519 woe_omZtf #> 12 Home NA 5 3 2 0.00496 0.00143 -1.24 woe_omZtf# 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 = 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#> # A tibble: 6 x 14 #> Seniority Time Age Marital Records Expenses Income Assets Debt Amount #> <int> <int> <int> <fct> <fct> <int> <int> <int> <int> <int> #> 1 0 48 41 married no 90 80 0 0 1200 #> 2 0 18 21 single yes 35 50 0 0 400 #> 3 0 36 23 single no 45 122 2500 0 400 #> 4 14 24 51 married no 75 198 1000 0 450 #> 5 1 60 26 single no 35 120 0 0 1150 #> 6 1 36 24 married no 76 164 0 0 900 #> # … with 4 more variables: Price <int>, Status <fct>, woe_Job <dbl>, #> # woe_Home <dbl>