Coarse classing lab: manual binning and manual variable choice
Source:R/classing.R
scr_coarse_classing.RdOpens a lab on an scr_select() result. Inside it the analyst inspects
the optimal bins of any binned variable (scr_classing_view()), proposes
new breaks or groupings (scr_classing_propose()), reads the comparison
against the optimal bins, accepts or discards each proposal with a
mandatory reason (scr_classing_accept(), scr_classing_discard()),
chooses the final variable list (scr_classing_choose()) and commits
everything to a new scr_result (scr_classing_apply()) that the rest
of the pipeline consumes unchanged: scr_scorecard(), scr_apply(),
scr_sql(), scr_export().
Usage
scr_coarse_classing(
x,
features = NULL,
laplace = 0,
max_iv_loss = NULL,
author = Sys.info()[["user"]]
)Arguments
- x
An object from
scr_select().- features
Variables the lab covers. Default: every variable that reached binning (
names(x$fit$results)), so a variable failed by screening can be rebinned and forced in with a reason.- laplace
Smoothing added to the bin counts when recomputing WOE.
0(default) is exactly the engine's formula.- max_iv_loss
Advisory threshold: a manual bin whose hold-out IV falls more than this fraction below the optimal one raises
IV_LOSS_VS_OPTIMAL.NULLusesconfig$lab_max_iv_loss.Free text recorded in the ledger.
Value
An scr_classing object (the lab), with a print method that
summarizes the session: variables touched, before/after IV, verdicts,
reasons, pending proposals and the final choice.
Contract of a manual bin
A manual bin is recomputed on the training rows only (hold-out rows
can never define a bin), with the engine's own WOE formula
(ln(%event / %non-event), event-oriented, so glm coefficients stay
positive), then revalidated on the hold-out with the bins frozen (IV, PSI
with both thresholds, unbinned share) and screened with the eight
engine rules, so the lab and the pipeline can never disagree. Numeric
intervals are right-closed, (a, b], exactly as the engine and its SQL.
Re-declaring the optimal cut points of a numeric reproduces the engine's
WOE exactly; for a categorical the engine applies a small internal
smoothing of its own, so the raw log-ratio of the lab differs from it in
the third decimal.
What is never allowed silently
An empty bin, a degenerate bin (no events or no non-events, unless
laplace > 0), a bin below lab_min_bin_pct_hard, a manual IV crossing
iv_max (the lab must not manufacture leakage), a category left
unassigned, a missing reason. Those block the proposal (BLOCKED);
accepting one needs override = TRUE, and the override is itself a
ledger row.
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")
lab <- scr_coarse_classing(res)
lab
#> <scr_classing> target "default" | opened 2026-10-02 00:02 by runner | 37 variables | 0 proposals: 0 accepted, 0 discarded
#> final choice: 12 variables | consensus 12 | force: (none) | drop: (none)
scr_classing_view(lab, "ds_region")
#> <scr_classing> ds_region (categorical) | current: optimal (jedi) | 5 bins | train IV 0.0846, hold-out IV 0.0971 (ratio 1.14)
#> monotone: yes | min bin 7.9% | PSI 0.0064 (stable) | KS 0.114 | degenerate bins: 0 | verdict: ACCEPTABLE
#> id bin n % events rate WOE IV | n.hold % rate WOE.hold
#> 1 CENTRE 539 19.2% 57 10.6% -0.341 0.020 | 301 21.5% 13.0% -0.130
#> 2 EAST 1,139 40.7% 144 12.6% -0.139 0.008 | 551 39.4% 13.8% -0.058
#> 3 WEST 582 20.8% 82 14.1% -0.014 0.000 | 269 19.2% 10.0% -0.419
#> 4 NORTH 318 11.4% 66 20.8% 0.453 0.027 | 178 12.7% 23.6% 0.599
#> 5 SOUTH 222 7.9% 50 22.5% 0.557 0.030 | 101 7.2% 18.8% 0.312
#> event rate by bin (train | hold-out)
#> 1 ######## 10.6% | ########## 13.0%
#> 2 ########## 12.6% | ########### 13.8%
#> 3 ########### 14.1% | ######## 10.0%
#> 4 ################ 20.8% | ################## 23.6%
#> 5 ################# 22.5% | ############## 18.8%
p <- scr_classing_propose(lab, "ds_region",
groups = list(edge = c("NORTH", "SOUTH"),
core = c("EAST", "WEST", "CENTRE")))
p
#> <scr_classing_proposal> P001 ds_region | groups = list(edge = c("NORTH", "SOUTH"), core = c("EAST", "WEST", "CENTRE")) | 2026-10-02 00:02
#> optimal manual delta
#> n_bins 5 2 -3
#> iv_train 0.0846 0.0739 -0.0107
#> iv_holdout 0.0971 0.0786 -0.0186
#> iv_ratio 1.1432 1.0568 -0.0864
#> ks 0.1141 0.1141 0.0000
#> psi 0.0064 0.0003 -0.0061
#> min_bin_pct 0.0793 0.1929 0.1136
#> largest_bin_pct 0.4068 0.8071 0.4004
#> n_degenerate 0 0 0
#> monotonic 1 1 0
#> manual bins (train | hold-out)
#> 1 NORTH | SOUTH 540 19.3% 21.5% 0.499 | 279 19.9% 21.9% 0.501
#> 2 EAST | WEST | CENTRE 2,260 80.7% 12.5% -0.149 | 1,121 80.1% 12.7% -0.156
#> Warnings
#> - IV_LOSS_VS_OPTIMAL
#> Verdict: REVIEW - advisory warnings only; accept with a reason or discard.
lab <- scr_classing_accept(lab, p, reason = "edge/core is what pricing uses")
lab <- scr_classing_choose(lab, drop = "vl_score_10",
reason = "not available at decision time")
lab
#> <scr_classing> target "default" | opened 2026-10-02 00:02 by runner | 37 variables | 1 proposals: 1 accepted, 0 discarded
#> variable action bins IV train IV hold-out verdict reason
#> ds_region accepted 5->2 0.0846->0.0739 0.0971->0.0786 REVIEW edge/core is what pricing uses
#> final choice: 11 variables | consensus 12 | force: (none) | drop: vl_score_10
res2 <- scr_classing_apply(lab)
scr_selected(res2)
#> [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"
scr_selected(res2, which = "consensus")
#> [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"
sc <- scr_scorecard(res2)
sc$model_card$binning_algorithm
#> [1] "jedi, manual"