Benchmarking

inst/benchmarks/run-benchmark.R reproduces a benchmark of fastgbm against gbm, xgboost, and ranger on six real survival datasets from the biostatlab package (pbc, heart_failure, breast, colon_cancer, crc_mondaca2020, framingham), under an equal-hyperparameter-grid regime with repeated 70/30 train/test splits.

# from the package root
Rscript inst/benchmarks/run-benchmark.R

This writes, under inst/benchmarks/:

• benchmark-results.csv – one row per (dataset, model, repeat), with training time, prediction time, and Harrell’s C-index;
• benchmark-summary.csv – median/IQR per (dataset, model), plus paired win/loss/tie counts of fastgbm against each competitor;
• parallel-speedup.csv – threads = 1 vs. threads = <hardware concurrency> training time on the largest dataset (framingham);
• session-info.txt – R/package versions, OS, seed, and hyperparameters, for reproducibility.

The full write-up, with tables and figures generated directly from these CSVs, is in paper/fastgbm-benchmark.qmd (renders to fastgbm-benchmark.pdf via quarto render). Results are reported honestly (win/loss/tie per dataset), not as a universal superiority claim – see the paper’s Comparative Summary section.