Select variables for the scorecard

Shortcut that chains scr_split(), scr_triage(), scr_bin() and scr_model() on a table and a binary target, and returns an object with the shortlist, the complete audit funnel, the gains table and the production SQL of the approved variables. Every stage remains callable on its own for whoever wants more control (hybrid interface).

Usage

scr_select(
  data,
  target,
  config = scr_config(),
  drop = character(),
  date_col = config$oot_date_col,
  event_level = NULL,
  export = NULL,
  copy = TRUE
)

Arguments

data

A data.frame or data.table with the target, the candidates and, if any, the date column of the out-of-time cut.

target

Name of the target column (0/1, logical, or a two-level factor/character).

config

An object from scr_config().

drop

Columns that are never candidates. They stay in the funnel as 00.config.

date_col

Date column of the out-of-time cut. Defaults to config$oot_date_col.

event_level

Which target value counts as the event; see scr_split().

export

Directory to write the deliverables to. NULL (default) writes nothing; use scr_export() later.

copy

If TRUE (default), works on a copy of data.

Value

An object of class scr_result. Read it with scr_selected(), scr_funnel(), scr_gains(), scr_sql(), scr_leakage() and summary(); continue with scr_scorecard(); write it with scr_export().

Reproducibility

With the same data, the same target and the same config$seed, the result is identical with one or several nthread: the seed governs the random split, the cross-validation, the classifier subsample, the trees and the bootstrap, and the binning is deterministic per column.

See also

scr_run() for several targets straight from the database, scr_scorecard() for the next step.

Other stages: scr_align(), scr_bin(), scr_cutoff(), scr_model(), scr_reject(), scr_scorecard(), scr_split(), scr_strategy(), scr_triage()

Examples

cfg <- scr_config(verbose = FALSE, nthread = 1, use_ranger = FALSE,
                  xgb_rounds = 60, n_boot = 20)
res <- scr_select(scr_demo, "default", config = cfg, drop = "id",
                  date_col = "ref_date")
res
#> <scr_result> target "default"
#>   4,200 rows (train 2,800 / hold-out 1,400) | split out-of-time at 2026-05-01
#>   event: 14.25% on train, 14.50% on hold-out | 0.8s
#>   convention: risk (target=1 is the bad case)
#> 
#> Funnel
#>   candidates        38 ############################
#>   1. triage         37 ###########################
#>   2. binning        37 ###########################
#>   3. screening      20 ###############
#>   4. hold-out       16 ############
#>   5. correlation    12 #########
#>   6. consensus      12 #########
#> 
#> Approved: 12
#>    1. vl_score_01                                  IV  0.346  KS 0.198
#>    2. vl_score_02                                  IV  0.172  KS 0.156
#>    3. vl_score_04                                  IV  0.124  KS 0.120
#>    4. ds_band                                      IV  0.081  KS 0.110
#>    5. vl_late                                      IV  0.071  KS 0.120
#>   ... (+7) - scr_selected() for the list
#> 
#> Models (hold-out)
#>   glmnet    AUC 0.7345 [0.7028, 0.7723]  KS 0.3842
#>   xgboost   AUC 0.7375 [0.7065, 0.7762]  KS 0.3695
#>   lightgbm  AUC 0.7342 [0.7028, 0.7750]  KS 0.3699
#> 
#> Warnings
#>   - 3 derived flag(s) outside the deliverable by policy (allow_derived_final)
scr_selected(res)
#>  [1] "vl_score_01" "vl_score_02" "vl_score_04" "ds_band"     "vl_late"    
#>  [6] "ds_region"   "vl_score_06" "vl_score_07" "vl_score_05" "ds_channel" 
#> [11] "vl_hist_04"  "vl_score_10"
head(scr_funnel(res, only_selected = TRUE))
#>        feature derived_from        type approved  exit_stage consensus_rank
#>         <char>       <char>      <char>   <lgcl>      <char>          <int>
#> 1: vl_score_01         <NA>     numeric     TRUE 07.approved              1
#> 2: vl_score_02         <NA>     numeric     TRUE 07.approved              2
#> 3: vl_score_04         <NA>     numeric     TRUE 07.approved              3
#> 4:     ds_band         <NA> categorical     TRUE 07.approved              4
#> 5:     vl_late         <NA>     numeric     TRUE 07.approved              5
#> 6:   ds_region         <NA> categorical     TRUE 07.approved              6
#>    consensus_score votes n_bins   total_iv iv_holdout        ks         psi
#>              <num> <int>  <int>      <num>      <num>     <num>       <num>
#> 1:       1.0000000     3      7 0.34639015 0.28772640 0.1981484 0.006635574
#> 2:       0.9090909     3      7 0.17206972 0.12336796 0.1564626 0.005346830
#> 3:       0.8181818     3      7 0.12430307 0.11773607 0.1199427 0.003216554
#> 4:       0.6967027     3      4 0.08054551 0.07804157 0.1100565 0.001317467
#> 5:       0.6065946     3      7 0.07109472 0.06384879 0.1201400 0.104196466
#> 6:       0.6057936     3      5 0.08464808 0.09714339 0.1141337 0.006365199
#>    psi_flag_adjusted iv_suspect triage_reason screen_reason holdout_reason
#>               <char>     <lgcl>        <char>        <char>         <char>
#> 1:            stable      FALSE            OK            OK             OK
#> 2:            stable      FALSE            OK            OK             OK
#> 3:            stable      FALSE            OK            OK             OK
#> 4:            stable      FALSE            OK            OK             OK
#> 5:             shift      FALSE            OK            OK             OK
#> 6:            stable      FALSE            OK            OK             OK
#>    prune_corr_with
#>             <char>
#> 1:            <NA>
#> 2:            <NA>
#> 3:            <NA>
#> 4:            <NA>
#> 5:            <NA>
#> 6:            <NA>