Creating a Scorecard

scorecard creates a scorecard based on the results from woebin and glm.

scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50,
  basepoints_eq0 = FALSE)

Arguments

bins	Binning information generated from woebin function.
model	A glm model object.
points0	Target points, default 600.
odds0	Target odds, default 1/19. Odds = p/(1-p).
pdo	Points to Double the Odds, default 50.
basepoints_eq0	Logical, Defaults to FALSE. If it is TRUE, the basepoints will equally distribute to each variable.

Value

A list of scorecard data frames

See also

scorecard2 scorecard_ply

Examples

# \donttest{
# load germancredit data
data("germancredit")

# filter variable via missing rate, iv, identical value rate
dt_sel = var_filter(germancredit, "creditability")
#> [INFO] filtering variables ... 

# woe binning ------
bins = woebin(dt_sel, "creditability")
#> [INFO] creating woe binning ... 
dt_woe = woebin_ply(dt_sel, bins)
#> [INFO] converting into woe values ... 

# glm ------
m = glm(creditability ~ ., family = binomial(), data = dt_woe)
# summary(m)

# Select a formula-based model by AIC
m_step = step(m, direction="both", trace=FALSE)
m = eval(m_step$call)
# summary(m)

# predicted proability
# dt_pred = predict(m, type='response', dt_woe)

# performace
# ks & roc plot
# perf_eva(dt_woe$creditability, dt_pred)

# scorecard
# Example I # creat a scorecard
card = scorecard(bins, m)
card2 = scorecard2(bins=bins, dt=germancredit, y='creditability',
  x=sub('_woe', '', names(coef(m))[-1]))

# credit score
# Example I # only total score
score1 = scorecard_ply(germancredit, card)

# Example II # credit score for both total and each variable
score2 = scorecard_ply(germancredit, card, only_total_score = F)
# }