A frozen set of candidate match pairs with a ground-truth equal label,
for the calibration workflow (fit_filter,
apply_filter, calibrate). It is a saved
search_candidates() run linking workshop_listings to
workshop_register, with equal filled from each listing's true
actual_link. The false positives are concentrated in the deliberately
hard tiers, common-surname homonyms and planted register duplicates, so a
learned filter has real mistakes to catch. The schema matches what
import_labels returns, so it feeds the calibration verbs with no
manual labelling step.
Format
A data frame with 1,862 rows (two rows per pair) and 12 variables:
- match_id
Integer pair id. Each id groups one
baserow and onetargetrow.- score
The candidate score from the frozen search
- source
"base"(the searched listing) or"target"(the register candidate). The candidate row carries the label.- id
The record id: a
listing_idon base rows, areg_noon target rows- workshop
Business name of the row
- proprietor
Proprietor name of the row
- trade
Trade
- postcode_area
UK outward-code area
- gen_tier
The generation tier of the row, useful for slicing the hard cases
- actual_link
The searched listing's true
reg_no(present on base rows)- rank
Candidate rank within the pair
- equal
Evaluation label.
1when the target candidate is the listing's true match,0otherwise. On base header rows it is the block default.
Source
Synthetically generated by data-raw/generate_match_labels.R
from the shipped workshop_listings / workshop_register pair.