automatedRecLin (version 1.0.1)

custom_rec_lin_model: Create a Custom Record Linkage Model

Description

Creates a supervised record linkage model using a custom machine learning (ML) classifier.

Usage

custom_rec_lin_model(ml_model, vectors)

Value

Returns a list containing:

• b_vars -- here NULL,

• cpar_vars -- here NULL,

• cnonpar_vars -- here NULL,

• b_params -- here NULL,

• cpar_params -- here NULL,

• cnonpar_params -- here NULL,

• ratio_kliep -- here NULL,

• ratio_kliep_list -- here NULL,

• ml_model -- ML model used for creating the record linkage model,

• pi_est -- a prior probability of matching,

• match_prop -- proportion of matches in the smaller dataset,

• variables -- a character vector of key variables used for comparison,

• comparators -- a list of functions used to compare pairs of records,

• methods -- here NULL,

• prob_ratio -- here "2".

Arguments

ml_model

A trained ML model that predicts the probability of a match based on comparison vectors.

vectors

An object of class comparison_vectors (a result of the comparison_vectors function), used for training the ml_model.

Author

Adam Struzik

Details

The custom_rec_lin_model function creates a custom record linkage model, based on known matches and non-matches (which might later serve as a classifier for pairs outside training data). The procedure of creating a custom model based on training data is as follows.

1. Use the comparison_vectors function to compare pairs of records.

2. Train a machine learning classifier using the Omega element of the output of the comparison_vectors function. The classifier should predict the probability of matching based on a given vector.

3. Use the custom_rec_lin_model function with appropriate arguments.

Examples

if (requireNamespace("xgboost", quietly = TRUE)) {
  df_1 <- data.frame(
    "name" = c("James", "Emma", "William", "Olivia", "Thomas",
    "Sophie", "Harry", "Amelia", "George", "Isabella"),
    "surname" = c("Smith", "Johnson", "Brown", "Taylor", "Wilson",
    "Davis", "Clark", "Harris", "Lewis", "Walker")
  )
  df_2 <- data.frame(
    "name" = c("James", "Ema", "Wimliam", "Olivia", "Charlotte",
    "Henry", "Lucy", "Edward", "Alice", "Jack"),
    "surname" = c("Smith", "Johnson", "Bron", "Tailor", "Moore",
    "Evans", "Hall", "Wright", "Green", "King")
  )
  comparators <- list("name" = jarowinkler_complement(),
                      "surname" = jarowinkler_complement())
  matches <- data.frame("a" = 1:4, "b" = 1:4)
  vectors <- comparison_vectors(A = df_1, B = df_2, variables = c("name", "surname"),
                               comparators = comparators, matches = matches)
  model_xgb <- xgboost::xgboost(x = as.matrix(vectors$Omega[, c("gamma_name", "gamma_surname")]),
                       y = factor(vectors$Omega$match),
                       objective = "binary:logistic", eval_metric = "logloss",
                       nrounds = 100, verbosity = 0)
  custom_xgb_model <- custom_rec_lin_model(model_xgb, vectors)
  custom_xgb_model
}