Bin drivers against a continuous target (LGD, CCF)

Supervised binning for a bounded continuous target, with the result in the shape of an obwoe object, so that the scr_apply() and scr_sql() machinery (OptimalBinningWoE::obwoe_apply() and OptimalBinningWoE::obwoe_sql()) reproduces the bin statistic unchanged. The woe slot of every bin carries the target mean of the bin (or its logit with scale = "logit"); iv carries the bin's share of the between-bin sum of squares, so total_iv is the eta-squared of the driver, in [0, 1].

Usage

scr_bin_continuous(
  data,
  target,
  features,
  train_idx = NULL,
  holdout_idx = NULL,
  min_bins = 2L,
  max_bins = 6L,
  min_share = 0.05,
  min_n = 30L,
  monotone = c("auto", "increasing", "decreasing", "none"),
  scale = c("mean", "logit"),
  nthread = 1L,
  alpha = 0.05
)

Arguments

data

A data.frame or data.table.

target

Column name of the continuous target.

features

Column names of the drivers.

train_idx, holdout_idx

Row indices; NULL uses every row for training and skips the revalidation.

min_bins, max_bins

Target range of bins per driver.

min_share

Minimum share of training rows per bin.

min_n

Minimum number of training rows per bin.

monotone

"auto" (direction from the Spearman sign), "increasing", "decreasing" or "none".

scale

"mean" (bin mean in the woe slot) or "logit".

nthread

Parallel workers by driver, through the package backend.

alpha

Alpha of the PSI critical value in the revalidation.

Value

An object of class scr_cbins: fit (the obwoe-shaped object), summary (one row per driver: feature, type, n_bins, eta2, direction, converged, and after revalidation eta2_holdout, psi, psi_flag, holdout_ok, holdout_reason), holdout (bin table per driver with train and hold-out means), scale and target. summary keeps the engine columns (algorithm, total_iv, iterations, error) and, after revalidation, psi_critical, psi_flag_adjusted and pct_unbinned.

Details

Numeric drivers must not contain missing values: run scr_triage() (or impute) first, exactly as the scorecard pipeline does. Categorical missing values become the level "NA", as in the engine. When a holdout_idx is given, the frozen bins are revalidated: the hold-out bin means are recomputed, the PSI of the bin shares is reported with the sample-size-adjusted critical value, a driver whose hold-out means break the training order is flagged UNSTABLE_HOLDOUT, one whose bin shares shift (fixed PSI flag "shift", PSI at or above 0.25) is flagged PSI_ACTION, and one with more than 1% of hold-out rows outside the bins UNBINNED_HOLDOUT.

See also

Other irb-ead: scr_ead(), scr_ead_data(), scr_ead_downturn(), scr_ead_validate()

Examples

set.seed(1)
d <- data.frame(x = runif(600), g = sample(c("a", "b", "c", "d"), 600, TRUE))
d$y <- pmin(1, pmax(0, 0.2 + 0.6 * d$x + (d$g == "d") * 0.2 + rnorm(600, 0, 0.1)))
cb <- scr_bin_continuous(d, "y", c("x", "g"), train_idx = 1:400, holdout_idx = 401:600)
cb
#> <scr_cbins> 2 driver(s) binned against 'y' (bin statistic: mean)
#>   x                        numerical   6 bins | eta2 0.578 | increasing | hold-out eta2 0.544, PSI 0.030 (stable)
#>   g                        categorical 4 bins | eta2 0.116 | ordered_by_mean | hold-out eta2 0.141, PSI 0.010 (stable) - UNSTABLE_HOLDOUT
cb$fit$results$x$bin
#> [1] "(-Inf;0.247727]"     "(0.247727;0.373063]" "(0.373063;0.486149]"
#> [4] "(0.486149;0.644316]" "(0.644316;0.847882]" "(0.847882;+Inf]"    
cb$fit$results$x$woe    # bin means of y
#> [1] 0.3399106 0.4612338 0.5029509 0.5907939 0.6592490 0.7884400