References
Chawla, Nitesh V, Kevin W Bowyer, Lawrence O Hall, and W Philip
Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling
Technique.” Journal of Artificial Intelligence Research
16: 321–57.
Chen, Tianqi, and Carlos Guestrin. 2016. “XGBoost: A Scalable Tree
Boosting System.” arXiv, ahead of print. https://doi.org/10.48550/ARXIV.1603.02754.
Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. 2015.
“Statistical Learning with Sparsity.” Monographs on
Statistics and Applied Probability 143 (143): 8.
Hvitfeldt, Emil. 2025. Themis: Extra Recipes Steps for Dealing with
Unbalanced Data. https://doi.org/10.32614/CRAN.package.themis.
Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to
Interpreting Model Predictions.” Advances in Neural
Information Processing Systems 30.
Pihur, Vasyl, Susmita Datta, and Somnath Datta. 2009. “RankAggreg,
an r Package for Weighted Rank Aggregation.” BMC
Bioinformatics 10 (1): 62.
Platt, John et al. 1999.
“Probabilistic Outputs for Support Vector Machines and Comparisons
to Regularized Likelihood Methods.” Advances in Large Margin
Classifiers 10 (3): 61–74.
Talhouk, Aline, Stefan Kommoss, Robertson Mackenzie, et al. 2016.
“Single-Patient Molecular Testing with NanoString nCounter Data
Using a Reference-Based Strategy for Batch Effect Correction.”
PLOS ONE 11 (4): e0153844. https://doi.org/10.1371/journal.pone.0153844.
Wright, Marvin N., and Andreas Ziegler. 2017.
“Ranger: A Fast Implementation of
Random Forests for High Dimensional Data in C++
and R.” Journal of Statistical
Software 77 (1). https://doi.org/10.18637/jss.v077.i01.