Score Transformation
scorecard_ply calculates credit score using the results from scorecard.
scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L, replace_blank_na = TRUE, var_kp = NULL)
dt |
A data frame, which is the original dataset for training model. |
card |
The scorecard generated from the function |
only_total_score |
Logical, Defaults to TRUE. If it is TRUE, then the output includes only total credit score; Otherwise, if it is FALSE, the output includes both total and each variable's credit score. |
print_step |
A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0, no message is print. |
replace_blank_na |
Logical. Replace blank values with NA. Defaults to TRUE. This argument should be the same with |
var_kp |
Name of force kept variables, such as id column. Defaults to NULL. |
A data frame in score values
# load germancredit data
data("germancredit")
# filter variable via missing rate, iv, identical value rate
dt_sel = var_filter(germancredit, "creditability")
# woe binning ------
bins = woebin(dt_sel, "creditability")
dt_woe = woebin_ply(dt_sel, bins)
# glm ------
m = glm(creditability ~ ., family = binomial(), data = dt_woe)
# Select a formula-based model by AIC
m_step = step(m, direction="both", trace=FALSE)
m = eval(m_step$call)
# 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 = FALSE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.