noauto_scorecard2 {autoScorecard}

Manually Input Parameters to Generate Scorecards The basic score is dispersed into each feature score

Description

Manually Input Parameters to Generate Scorecards The basic score is dispersed into each feature score

Usage

noauto_scorecard2(
  bins_card,
  fit,
  bins_woe,
  points0 = 600,
  odds0 = 1/19,
  pdo = 50,
  k = 3
)

Arguments

bins_card

Binning template.

fit

See glm stats.

bins_woe

Base point.

points0

odds.

odds0

Point-to Double Odds.

pdo

A data frame of woe with independent variables and target variable.

k

Each scale doubles the probability of default several times.

Value

A data frame with score ratings.

Examples

accepts <- read.csv( system.file( "extdata", "accepts.csv" , package = "autoScorecard" ))
feature <- stats::na.omit( accepts[,c(1,3,7:23)] )
d = sort( sample( nrow( feature ), nrow( feature )*0.7))
train <- feature[d,]
test <- feature[-d,]
treebins_train <- bins_tree( df = train, key_var = "application_id", y_var="bad_ind",
max_depth=3, p=0.1)
woe_train <- rep_woe( df= train , key_var = "application_id", y_var = "bad_ind" ,
tool = treebins_train ,var_label = "variable",col_woe = 'woe', lower = 'lower' , upper = 'upper')
woe_test <- rep_woe(  df = test , key_var ="application_id", y_var= "bad_ind",
tool = treebins_train ,var_label= "variable",
    col_woe = 'woe', lower = 'lower' ,upper = 'upper'  )
lg <- stats::glm( bad_ind~. , family = stats::binomial( link = 'logit' ) , data = woe_train )
lg_both <- stats::step( lg , direction = "both")
Score2 <- noauto_scorecard2( bins_card= woe_test , fit =lg_both , bins_woe = treebins_train ,
points0 = 600 , odds0 = 1/20 , pdo = 50 )