Create a Scorecard Model Object

scorecard() creates a scorecard object using a fitted logistic model and card specifications. This function is designed to work with models that have been created using Weight of Evidence (WoE) features, specifically with a logistic regression model of class inheriting from "glm" using the binomial family and logit link.

Usage

scorecard(fit, card)

Arguments

fit

A fitted logistic regression model (of class inheriting from "glm") with WoE features. The model should belong to the binomial family and use the logit link.

card

A data frame containing card specifications. The card data frame must have the following columns: variable, class, woe, and points. The variable column should be expressed in raw terms, i.e., not as WoE features.

Value

A scorecard object.

Examples

# Reverse levels of `default_status`
loans$default_status <- factor(loans$default_status, levels = c("good", "bad"))

# Pre-process the data to create WoE features
train <- woe(
  data = loans,
  outcome = default_status,
  predictors = c(industry, housing_status),
  method = "replace",
  verbose = FALSE
)

# Fit the logistic regression model
my_model <- glm(
  formula = default_status ~ .,
  data = train,
  family = "binomial"
)

# Assume we have the following associated card
my_card <- tibble::tribble(
  ~variable, ~class, ~woe, ~points,
  "industry", "", 1.23, 148,
  "industry", "beef", -0.231, 107,
  "industry", "dairy", -0.0956, 110,
  "industry", "fruit", -0.359, 103,
  "industry", "grain", 0.410, 125,
  "industry", "greenhouse", -0.511, 99,
  "industry", "nuts", -0.288, 105,
  "industry", "pork", -0.606, 96,
  "industry", "poultry", 0.774, 135,
  "industry", "sod", -0.154, 109,
  "housing_status", "own", 0.194, 119,
  "housing_status", "rent", -0.430, 101
)

# Create the scorecard object
my_scorecard <- scorecard(
  fit = my_model,
  card = my_card
)