bioLeak (version 0.2.0)

summary.LeakFit: Summarize a LeakFit object

Description

Prints a compact console report for a [LeakFit] object created by [fit_resample()]. The report lists task/outcome metadata, learners, total folds, and cross-validated metrics summarized as mean and standard deviation across completed folds, plus a small audit table with per-fold train/test sizes and retained feature counts.

Usage

# S3 method for LeakFit
summary(object, digits = 3, ...)

Value

Invisibly returns `object@metric_summary`, a data frame of per-learner metric means and standard deviations computed across folds. This function does not recompute metrics.

Arguments

object

A [LeakFit] object returned by [fit_resample()]. It should contain `metric_summary` and `audit` slots; missing entries result in empty sections in the printed report.

digits

Integer scalar. Number of decimal places to print in numeric summary tables. Defaults to 3; affects printed output only, not the returned data.

...

Unused. Included for S3 method compatibility; changing these values has no effect.

Details

This summary is meant for quick sanity checks of the resampling setup and performance. It does not run leakage diagnostics and will not detect target leakage, duplicate samples, or batch/study confounding; use [audit_leakage()] or `summary()` on a [LeakAudit] object for those checks.

Examples

set.seed(1)
df <- data.frame(
  subject = rep(1:6, each = 2),
  outcome = factor(rep(c(0, 1), each = 6)),
  x1 = rnorm(12),
  x2 = rnorm(12)
)
splits <- make_split_plan(
  df,
  outcome = "outcome",
  mode = "subject_grouped",
  group = "subject",
  v = 3,
  stratify = TRUE,
  progress = FALSE
)
custom <- list(
  glm = list(
    fit = function(x, y, task, weights, ...) {
      stats::glm(y ~ ., data = data.frame(y = y, x),
                 family = stats::binomial(), weights = weights)
    },
    predict = function(object, newdata, task, ...) {
      as.numeric(stats::predict(object,
                                newdata = as.data.frame(newdata),
                                type = "response"))
    }
  )
)
fit <- fit_resample(df, outcome = "outcome", splits = splits,
                    learner = "glm", custom_learners = custom,
                    metrics = "auc", seed = 1)
summary_df <- summary(fit)
summary_df