Detect Potential Data Leakage — detect

Identifies potential sources of data leakage prior to predictive modeling. The assessment includes identifier columns, duplicate columns, high-cardinality features, predictors identical to the target, and predictors showing an almost perfect correlation with the target.

Usage

detect_leakage(data, target = NULL)

Arguments

data: A data.frame to assess.
target: Optional name of the target variable. If supplied, predictors identical to the target or nearly perfectly correlated with the target are flagged as potential leakage.

Value

A named list containing:

identifier_columns: Names of columns identified as potential identifiers.
duplicate_columns: Names of duplicated columns.
high_cardinality_columns: Names of high-cardinality columns.
target_leakage: Names of predictors identical to the target variable.
correlation_leakage: Numeric predictors with near-perfect correlation to the target.
leakage_score: Overall leakage score ranging from 0 to 100.
variables: A data frame containing variable-level leakage diagnostics, including identifier, duplicate, high-cardinality, target leakage, correlation leakage, and leakage score.

Examples

# Supervised leakage detection
res <- detect_leakage(airquality, target = "Ozone")
res$leakage_score
#> [1] 100
res$correlation_leakage
#> character(0)

# Unsupervised check for IDs and duplicates
res_unsupervised <- detect_leakage(iris)
res_unsupervised$identifier_columns
#> [1] "Sepal.Width" "Petal.Width"