Trains the classifiers enabled in the configuration on the WOE columns of the eligible pool, measures each on the hold-out (AUC/KS/Gini with a bootstrap CI) and combines the votes:
Usage
scr_model(bins, config = scr_config())Arguments
- bins
An object from
scr_bin().- config
An object from
scr_config().
Value
An scr_models object with votes (one row per model and
feature), metrics (one row per model, with CI), consensus (table,
selected, meta) and the originating bins.
Details
consensus_score = mean of the importance rank percentiles, weighted by the
hold-out Gini of each model
votes = how many models elected the feature (top-K, or non-zero
coefficient in the elastic net)
The final cut respects [target_min, target_max]. If the strict
consensus does not reach target_min, relaxation happens in named,
recorded steps (min_votes reduced; completed by score), never
resurrecting a feature failed by an earlier gate.
See also
Other stages:
scr_align(),
scr_bin(),
scr_cutoff(),
scr_reject(),
scr_scorecard(),
scr_select(),
scr_split(),
scr_strategy(),
scr_triage()
Examples
cfg <- scr_config(verbose = FALSE, nthread = 1, use_ranger = FALSE,
xgb_rounds = 60, n_boot = 20)
sp <- scr_split(scr_demo, "default", date_col = "ref_date", drop = "id")
#> OOT: 4 period(s) in train, 2 in hold-out (hold-out starts at 2026-05-01, 33.3% of rows)
md <- scr_model(scr_bin(scr_triage(sp, cfg), cfg), cfg)
md
#> <scr_models> 3 model(s) | pool 12 | approved 12 | relaxation: none
#> glmnet AUC 0.7345 [0.7028, 0.7723] KS 0.3842 votes 12
#> xgboost AUC 0.7375 [0.7065, 0.7762] KS 0.3695 votes 12
#> lightgbm AUC 0.7342 [0.7028, 0.7750] KS 0.3699 votes 12
md$consensus$selected
#> [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"