Pipeline configuration

Builds the configuration object that crosses every stage, from scr_split() to scr_export(). A preset sets the tightness of the selection funnel; any individual key can be overridden through ....

Usage

scr_config(preset = c("moderate", "aggressive", "lazy"), ...)

Arguments

preset: One of "moderate" (default), "aggressive" or "lazy".
...: Overrides of any configuration key, by name. NULL means "keep the preset value", not "delete the key". An unknown name is an error, on purpose: a silent override leaves dead configuration in the file.

Value

An object of class scr_config: a named list with every key resolved.

Presets

A preset touches four keys and nothing else: target_max, min_votes, corr_cutoff and iv_min.

preset	variables at the end	`min_votes`	`corr_cutoff`	`iv_min`
`"aggressive"`	10 to 15	3	0.60	0.03
`"moderate"`	10 to 25	2	0.70	0.02
`"lazy"`	10 to 40	1	0.80	0.02

Use scr_presets() to see the resolved table and scr_config_keys() for the full key dictionary.

Risk or propensity (`objective`)

The two literatures use the same mathematics with opposite conventions. In credit and fraud, target = 1 is the bad case and the scorecard is built so that more points mean less risk. In propensity, target = 1 is the good case and the campaign list needs more points to mean a higher chance of engaging.

	`"risk"` (default)	`"propensity"`
`target = 1` means	undesirable event	desirable event
Points scale	more points = safer	more points = more likely
Derived `direction`	`"higher_is_safer"`	`"higher_is_riskier"`
`odds_orientation`	`safe:event`	`event:safe`

objective does not touch the selection. It does not change the modeled target, the cut points, the IV or the shortlist; it acts on the direction of the points scale, on the odds orientation of the alignment and on the vocabulary of the reports. To model the other class as the event, the argument is event_level in scr_split() and scr_select(), and that one, unlike this, rewrites everything.

Binning algorithm (`algorithm`)

"jedi" is the default and stays exposed side by side with the alternatives, never hidden behind an "auto". Choices with distinct properties: "ivb", "dp" and "sblp" are provably optimal for categoricals; "cm" (ChiMerge) and "fetb" have a principled stopping rule; "ir" (isotonic) guarantees monotone WOE; "fast_mdlp" is the faithful Fayyad-Irani. The full list is in OptimalBinningWoE::obwoe_algorithms(). A numeric-only or categorical-only algorithm applies where it is valid and the other type falls back to "jedi".

The Information Value gate

iv_min: Admission floor. Fails with IV_BELOW_MIN.
iv_max: Admission ceiling. Fails with IV_SUSPICIOUS. 1.00 (default) tolerates a legitimately strong predictor and cuts the absurd; 0.50 is the engine default, calibrated for credit default.
iv_suspect: Only the threshold of the report warning. Fails nothing.

The real leakage detector is allow_degenerate = FALSE: a bin with no events or no non-events is the symptom that has no innocent explanation.

Scorecard scale

base_score, base_odds and pdo are one statement: at base_score points the odds are base_odds, and every pdo points they double. base_odds is always expressed in the orientation direction implies (non-event:event under higher_is_safer; event:non-event under higher_is_riskier), and the alignment object records odds_orientation so this is never implicit. The classic 600/50/20 is Siddiqi's (2006) textbook example, not a parameter published by any bureau. align_method = "regression" (default) takes the raw score to that scale by regressing empirical log-odds on score bands, which absorbs reweighting, miscalibration and prior shift; "direct" assumes the model is calibrated and uses the logit as is.

References

Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Wiley.

Examples

cfg <- scr_config()
cfg
#> <scr_config> preset "moderate" | objective "risk" | seed 2203 | threads 2
#> 
#> Convention
#>   target = 1             target = 1 is the BAD case
#>   points scale           more points = lower probability of the event (safer) [higher_is_safer]
#> 
#> Funnel
#>   variables at the end   10 to 25
#>   minimum votes          2
#>   admissible IV          [0.02, 1)  warning at 0.5
#>   correlation            0.7 (spearman)
#> 
#> Binning
#>   bins                   3 to 7, algorithm "jedi"
#>   monotonicity           numeric (weak)
#>   smallest bin           2.0%
#> 
#> Scorecard
#>   scale                  600 points at odds 50:1, PDO 20
#>   alignment              regression (10 bands)
#>   challenger             none
#>   bootstrap CI           200 resamples, 95%
#> 
#> Data
#>   sentinels              -999
#>   derived at the end     no (diagnostic only)
#>   hold-out               30.0%
#> 
#> Models
#>   enabled                glmnet, xgboost, ranger, lightgbm
#>   row cap                200,000

# propensity: more points = more likely to have the event
scr_config(objective = "propensity")$objective
#> [1] "propensity"

# NULL keeps the preset value (here, iv_min = 0.03 from aggressive)
scr_config("aggressive", iv_min = NULL)$iv_min
#> [1] 0.03

# a wrong name fails loudly instead of becoming dead configuration
try(scr_config(iv_maximum = 1))
#> Error : scr_config(): unknown key(s): iv_maximum. Fix the name - a silent override hides dead configuration.