Confusion Matrix at a Threshold — il_confusion

Computes the thresholded confusion-matrix counts for labeled pairs. Uses the same SQL-first scoring path as il_accuracy() on supported backends, so labeled pairs do not need to be predicted and collected in full before evaluation.

Usage

il_confusion_matrix(model, labels = NULL, threshold = 0.85, labels_col = NULL)

Arguments

model: A trained il_model object.
labels: A data frame of labeled pairs with a logical or integer match indicator. Required unless labels_col is provided.
threshold: A numeric value between 0 and 1 for classifying pairs as matches. Defaults to 0.85.
labels_col: Optional string naming a column in the original data containing ground-truth cluster/entity IDs. When provided, pairwise labels are derived automatically via labels_from_column().

Value

A one-row tibble containing threshold, tp, fp, fn, tn, fn_blocking_miss, precision, recall, and f1.

Examples

df <- data.frame(
  unique_id = 1:20,
  first_name = c(
    'John', 'Jon', 'Jane', 'Jane', 'Bob',
    'Bobby', 'Alice', 'Alicia', 'Tom', 'Thomas',
    'John', 'Jon', 'Jane', 'Janet', 'Bob',
    'Robert', 'Alice', 'Alison', 'Tom', 'Tomas'
  ),
  surname = c(
    'Smith', 'Smith', 'Doe', 'Doe', 'Jones',
    'Jones', 'Brown', 'Brown', 'White', 'White',
    'Smith', 'Smyth', 'Doe', 'Doe', 'Jones',
    'Jones', 'Brown', 'Browne', 'White', 'White'
  ),
  dob = c(
    '1990-01-01', '1990-01-01', '1985-06-15', '1985-06-15',
    '2000-12-01', '2000-12-01', '1975-03-22', '1975-03-22',
    '1988-07-04', '1988-07-04', '1990-01-01', '1990-01-02',
    '1985-06-15', '1985-06-16', '2000-12-01', '2000-12-02',
    '1975-03-22', '1975-03-23', '1988-07-04', '1988-07-05'
  )
)
con <- DBI::dbConnect(duckdb::duckdb())
spec <- il_spec() |>
  il_compare(first_name, cl_jaro_winkler(0.9, 0.7)) |>
  il_compare(surname, cl_jaro_winkler(0.9, 0.7)) |>
  il_compare(dob, cl_exact()) |>
  il_block_on(surname) |>
  il_block_on(first_name)
model <- il_model(df, spec = spec, con = con)
model <- il_estimate_u(model)
model <- il_estimate_em(model, block_on(surname))
#> EM trained: first_name and dob | skipped (blocked on): surname
labels <- data.frame(
  unique_id_l = c(1L, 1L),
  unique_id_r = c(11L, 2L),
  is_match = c(1L, 0L)
)

il_confusion_matrix(model, labels = labels, threshold = 0.85)
#> # A tibble: 1 × 9
#>   threshold    tp    fp    fn    tn fn_blocking_miss precision recall    f1
#>       <dbl> <int> <int> <int> <int>            <int>     <dbl>  <dbl> <dbl>
#> 1      0.85     1     1     0     0                0       0.5      1 0.667
DBI::dbDisconnect(con, shutdown = TRUE)