Rating grades on the score

Cuts the production score into grades whose PD is monotone. The grade boundaries are score cut points, direction-aware: grade 1 is the safest (the highest scores under higher_is_safer). Three constructions: "geometric" builds a scr_master_scale() between percentiles 1 and 99 of the calibrated PD and converts its PD bounds into scores through the calibrated alignment; "quantile" cuts equal-count score bands (cut points moved half-way between neighboring scores, so a boundary never sits on an observed value); "supplied" grades by the PD bands of a given master scale.

Usage

scr_grades(
  x,
  calibration = NULL,
  master_scale = NULL,
  n_grades = NULL,
  method = NULL,
  min_obligors = NULL,
  min_defaults = NULL,
  monotone = TRUE,
  pd_source = NULL,
  sample = "holdout",
  dr = NULL
)

Arguments

x: An scr_scorecard().
calibration: An scr_calibrate() object (or its alignment); NULL uses the scorecard's own alignment.
master_scale: An scr_master_scale() for method = "supplied" (optional for "geometric").
n_grades, method, min_obligors, min_defaults, pd_source: NULL reads pd_n_grades, pd_grade_method, pd_min_obligors, pd_min_defaults and pd_source from the scorecard configuration.
monotone: Repair non-monotone grade PDs by pooling.
sample: Sample of the scorecard used to build the grades.
dr: Optional scr_dr with a grade column keyed by the final grades (see the section above).

Value

An object of class scr_grades: table (grade, label, score_lo, score_hi, pd_lo, pd_hi, n, share, defaults, dr, pd_mean, pd_be, merged_from, and n_series, t_series when a series is given), breaks (ascending score cut points), band_grade (grade of every score band, ascending), direction, method, pd_source, master_scale, alignment (calibrated), alignment_score (the scorecard's), concentration (hhi, cv, hi, k), repairs, ledger, moc (empty, filled by scr_moc()), dr (the pooled series), rows (score, outcome and grade of the sample), scorecard, sample, ct, sample_rate. Also calibration (the scr_pd_calibration when one was given), n_grades_requested, min_obligors, min_defaults, target and config.

Details

Grades below min_obligors obligors or min_defaults defaults are merged with the neighbor of closer default rate; the sequence of grade PDs is then repaired by pool-adjacent-violators when monotone = TRUE, and every merge is recorded in repairs. The grade PD (pd_be) is the long-run average of the grade default rates when a default-rate series by grade is given in dr (pd_source = "lra"), the sample default rate of the grade otherwise, or the mean of the calibrated individual PDs (pd_source = "mean_pd"). Concentration is reported as the Herfindahl index, the coefficient of variation of the grade shares and the Herfindahl-based hi index.

Two-pass workflow with a default-rate series

The series must be keyed by the final grades of this same call. Run scr_grades() once, grade the cohort panel with predict.scr_grades(), build the series with scr_default_rate() (grade =) and pass it as dr in a second call with identical arguments (or in scr_moc() and scr_pd_validate(), which read it the same way).

Examples

cfg <- scr_config(verbose = FALSE, nthread = 1, use_ranger = FALSE,
                  use_lightgbm = FALSE, xgb_rounds = 40, n_boot = 10)
res <- scr_select(scr_demo, "default", config = cfg, drop = c("id", "churn"),
                  date_col = "ref_date")
sc <- scr_scorecard(res)
cal <- scr_calibrate(sc, target = 0.06)
gr <- scr_grades(sc, cal, n_grades = 7, min_defaults = 10)
gr
#> <scr_grades> target "default" | 5 grades (geometric) on holdout | PD source: lra (sample default rate; pass `dr` for the series) | higher_is_safer
#>   concentration: HHI 0.250 | CV 0.500 | HI 0.139 | repairs 2 | calibrated to CT 6.000%
#>   grade label    score_lo  score_hi      n  share   def       dr  pd_mean    pd_be
#>   1     1+2+3      569.06       Inf    369  26.4%    13    3.52%    1.14%    3.52%
#>   2     4          545.99    569.06    410  29.3%    39    9.51%    3.17%    9.51%
#>   3     5          522.00    545.99    390  27.9%    76   19.49%    6.67%   19.49%
#>   4     6          495.73    522.00    174  12.4%    55   31.61%   14.12%   31.61%
#>   5     7            -Inf    495.73     57   4.1%    20   35.09%   28.40%   35.09%
#>   repair (min_counts): 1 -> 2 | n 46, defaults 2 (minimum 30 / 10)
#>   repair (min_counts): 1+2 -> 3 | n 148, defaults 4 (minimum 30 / 10)
gr$table[, c("grade", "score_lo", "score_hi", "n", "dr", "pd_be")]
#>    grade score_lo score_hi     n         dr      pd_be
#>    <int>    <num>    <num> <int>      <num>      <num>
#> 1:     1 569.0615      Inf   369 0.03523035 0.03523035
#> 2:     2 545.9895 569.0615   410 0.09512195 0.09512195
#> 3:     3 522.0009 545.9895   390 0.19487179 0.19487179
#> 4:     4 495.7314 522.0009   174 0.31609195 0.31609195
#> 5:     5     -Inf 495.7314    57 0.35087719 0.35087719
# grade a cohort panel with the score cut points (the panel score is a
# different scale; the demo only shows the mechanics)
head(predict(gr, score = scr_demo_panel$score))
#> [1] 1 1 1 1 1 1