A synthetic per-observation error stream from a credit-approval
classifier in production: 500 stable observations (5% error rate),
then 500 after a market shift raised the error rate to 30%. Frozen
with a fixed seed, so every example that loads it sees the same
story — including the single, correct DDM detection at t = 542
with no false positives before it.
Format
A tibble with 1,000 rows and 3 columns:
- t
Observation index, 1 to 1000.
- error
0/1 classifier error for that observation.
- drift_true
Ground truth:
TRUEfrom observation 501 on, the point the market shift occurred.
Source
Simulated with sim_drift_stream():
sim_drift_stream(n_pre = 500, n_post = 500, p_pre = 0.05, p_post = 0.30, seed = 2).
See data-raw/credit-monitoring.R.
Examples
credit_monitoring
#> # A tibble: 1,000 × 3
#> t error drift_true
#> <int> <int> <lgl>
#> 1 1 0 FALSE
#> 2 2 0 FALSE
#> 3 3 0 FALSE
#> 4 4 0 FALSE
#> 5 5 0 FALSE
#> 6 6 0 FALSE
#> 7 7 0 FALSE
#> 8 8 0 FALSE
#> 9 9 0 FALSE
#> 10 10 0 FALSE
#> # ℹ 990 more rows
detect_drift(credit_monitoring, .col = error, method = "ddm")
#> # A tibble: 1,000 × 5
#> t error drift_true .warning .drift
#> <int> <int> <lgl> <lgl> <lgl>
#> 1 1 0 FALSE NA NA
#> 2 2 0 FALSE NA NA
#> 3 3 0 FALSE NA NA
#> 4 4 0 FALSE NA NA
#> 5 5 0 FALSE NA NA
#> 6 6 0 FALSE NA NA
#> 7 7 0 FALSE NA NA
#> 8 8 0 FALSE NA NA
#> 9 9 0 FALSE NA NA
#> 10 10 0 FALSE NA NA
#> # ℹ 990 more rows