References

Ahmad, Tanvir, Assia Munir, Sajjad Haider Bhatti, Muhammad Aftab, and Muhammad Ali Raza. 2017. “Survival Analysis of Heart Failure Patients: A Case Study.” PLOS ONE 12 (7): e0181001. https://doi.org/10.1371/journal.pone.0181001.
Ambroise, Christophe, and Geoffrey J. McLachlan. 2002. “Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data.” Proceedings of the National Academy of Sciences 99 (10): 6562–66. https://doi.org/10.1073/pnas.102102699.
Angelopoulos, Anastasios N., and Stephen Bates. 2023. “Conformal Prediction: A Gentle Introduction.” Foundations and Trends® in Machine Learning 16 (4): 494–591. https://doi.org/10.1561/2200000101.
Apley, Daniel W., and Jingyu Zhu. 2020. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” Journal of the Royal Statistical Society Series B: Statistical Methodology 82 (4): 1059–86. https://doi.org/10.1111/rssb.12377.
Arik, Sercan Ö., and Tomas Pfister. 2021. “TabNet: Attentive Interpretable Tabular Learning.” Proceedings of the AAAI Conference on Artificial Intelligence 35 (8): 6679–87. https://doi.org/10.1609/aaai.v35i8.16826.
Ayer, Miriam, H. D. Brunk, G. M. Ewing, W. T. Reid, and Edward Silverman. 1955. “An Empirical Distribution Function for Sampling with Incomplete Information.” The Annals of Mathematical Statistics 26 (4): 641–47. https://doi.org/10.1214/aoms/1177728423.
Barber, Rina Foygel, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani. 2021. “Predictive Inference with the Jackknife+.” The Annals of Statistics 49 (1): 486–507. https://doi.org/10.1214/20-aos1965.
Bates, Stephen, Trevor Hastie, and Robert Tibshirani. 2024. “Cross-Validation: What Does It Estimate and How Well Does It Do It?” Journal of the American Statistical Association 119 (546): 1434–45. https://doi.org/10.1080/01621459.2023.2197686.
Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019. “Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off.” Proceedings of the National Academy of Sciences 116 (32): 15849–54. https://doi.org/10.1073/pnas.1903070116.
Bengio, Yoshua, and Yves Grandvalet. 2004. “No Unbiased Estimator of the Variance of K-Fold Cross-Validation.” Journal of Machine Learning Research 5: 1089–105.
Benkeser, David, and Mark J. van der Laan. 2016. “The Highly Adaptive Lasso Estimator.” 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), October, 689–96. https://doi.org/10.1109/dsaa.2016.93.
Bergstra, James, and Yoshua Bengio. 2012. “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research 13: 281–305.
Bezdek, James C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer US. https://doi.org/10.1007/978-1-4757-0450-1.
Boser, Bernhard E., Isabelle M. Guyon, and Vladimir N. Vapnik. 1992. “A Training Algorithm for Optimal Margin Classifiers.” Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT92, 144–52. https://doi.org/10.1145/130385.130401.
Box, G. E. P., and D. R. Cox. 1964. “An Analysis of Transformations.” Journal of the Royal Statistical Society Series B: Statistical Methodology 26 (2): 211–43. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x.
Breiman, Leo. 1996a. “Bagging Predictors.” Machine Learning 24 (2): 123–40. https://doi.org/10.1007/bf00058655.
Breiman, Leo. 1996b. “Stacked Regressions.” Machine Learning 24 (1): 49–64. https://doi.org/10.1007/bf00117832.
Breiman, Leo. 2001a. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/a:1010933404324.
Breiman, Leo. 2001b. “Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author).” Statistical Science 16 (3). https://doi.org/10.1214/ss/1009213726.
Breiman, Leo, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone. 1984. Classification and Regression Trees. Wadsworth.
Brier, Glenn W. 1950. “Verification of Forecasts Expressed in Terms of Probability.” Monthly Weather Review 78 (1): 1–3. https://doi.org/10.1175/1520-0493(1950)0782.0.co;2.
Caliński, T., and J Harabasz. 1974. “A Dendrite Method for Cluster Analysis.” Communications in Statistics 3 (1): 1–27. https://doi.org/10.1080/03610927408827101.
Cawley, Gavin C., and Nicola L. C. Talbot. 2010. “On over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–107.
Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research 16: 321–57. https://doi.org/10.1613/jair.953.
Chen, Tianqi, and Carlos Guestrin. 2016. “XGBoost: A Scalable Tree Boosting System.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, August, 785–94. https://doi.org/10.1145/2939672.2939785.
Chicco, Davide, and Giuseppe Jurman. 2020. “Machine Learning Can Predict Survival of Patients with Heart Failure from Serum Creatinine and Ejection Fraction Alone.” BMC Medical Informatics and Decision Making 20 (1). https://doi.org/10.1186/s12911-020-1023-5.
Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010. “BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1): 266–98. https://doi.org/10.1214/09-aoas285.
Christodoulou, Evangelia, Jie Ma, Gary S. Collins, Ewout W. Steyerberg, Jan Y. Verbakel, and Ben Van Calster. 2019. “A Systematic Review Shows No Performance Benefit of Machine Learning over Logistic Regression for Clinical Prediction Models.” Journal of Clinical Epidemiology 110: 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004.
Chun, Hyonho, and Sündüz Keleş. 2010. “Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection.” Journal of the Royal Statistical Society Series B: Statistical Methodology 72 (1): 3–25. https://doi.org/10.1111/j.1467-9868.2009.00723.x.
Collins, G. S., J. B. Reitsma, D. G. Altman, and K. G. M. Moons. 2015. “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.” BMJ 350 (jan07 4): g7594–94. https://doi.org/10.1136/bmj.g7594.
Collins, Gary S, Karel G M Moons, Paula Dhiman, et al. 2024. “TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods.” BMJ 385 (April): e078378. https://doi.org/10.1136/bmj-2023-078378.
Cortes, Corinna, and Vladimir Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–97. https://doi.org/10.1007/bf00994018.
Cover, T., and P. Hart. 1967. “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory 13 (1): 21–27. https://doi.org/10.1109/tit.1967.1053964.
COX, D. R. 1958. “Two Further Applications of a Model for Binary Regression.” Biometrika 45 (3-4): 562–65. https://doi.org/10.1093/biomet/45.3-4.562.
Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society Series B: Statistical Methodology 39 (1): 1–22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.
Efron, B. 1979. “Bootstrap Methods: Another Look at the Jackknife.” The Annals of Statistics 7 (1). https://doi.org/10.1214/aos/1176344552.
Efron, Bradley. 1986. “How Biased Is the Apparent Error Rate of a Prediction Rule?” Journal of the American Statistical Association 81 (394): 461–70. https://doi.org/10.1080/01621459.1986.10478291.
Efron, Bradley, Trevor Hastie, Iain Johnstone, and Robert Tibshirani. 2004. “Least Angle Regression.” The Annals of Statistics 32 (2). https://doi.org/10.1214/009053604000000067.
Efron, Bradley, and Robert Tibshirani. 1997. “Improvements on Cross-Validation: The 632+ Bootstrap Method.” Journal of the American Statistical Association 92 (438): 548–60. https://doi.org/10.1080/01621459.1997.10474007.
Eilers, Paul H. C., and Brian D. Marx. 1996. “Flexible Smoothing with b-Splines and Penalties.” Statistical Science 11 (2). https://doi.org/10.1214/ss/1038425655.
Elkan, Charles. 2001. “The Foundations of Cost-Sensitive Learning.” Proceedings of the 17th International Joint Conference on Artificial Intelligence, 973–78.
Ester, Martin, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 226–31.
Fawcett, Tom. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters 27 (8): 861–74. https://doi.org/10.1016/j.patrec.2005.10.010.
Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. 2019. “All Models Are Wrong, but Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously.” Journal of Machine Learning Research 20 (177): 1–81.
Fraley, Chris, and Adrian E Raftery. 2002. “Model-Based Clustering, Discriminant Analysis, and Density Estimation.” Journal of the American Statistical Association 97 (458): 611–31. https://doi.org/10.1198/016214502760047131.
Freund, Yoav, and Robert E Schapire. 1997. “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences 55 (1): 119–39. https://doi.org/10.1006/jcss.1997.1504.
Friedman, Jerome H. 1991. “Multivariate Adaptive Regression Splines.” The Annals of Statistics 19 (1). https://doi.org/10.1214/aos/1176347963.
Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5). https://doi.org/10.1214/aos/1013203451.
Friedman, Jerome H. 2002. “Stochastic Gradient Boosting.” Computational Statistics &Amp; Data Analysis 38 (4): 367–78. https://doi.org/10.1016/s0167-9473(01)00065-2.
Friedman, Jerome H., and Bogdan E. Popescu. 2008. “Predictive Learning via Rule Ensembles.” The Annals of Applied Statistics 2 (3): 916–54. https://doi.org/10.1214/07-aoas148.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software 33 (1). https://doi.org/10.18637/jss.v033.i01.
Geman, Stuart, Elie Bienenstock, and René Doursat. 1992. “Neural Networks and the Bias/Variance Dilemma.” Neural Computation 4 (1): 1–58. https://doi.org/10.1162/neco.1992.4.1.1.
Geurts, Pierre, Damien Ernst, and Louis Wehenkel. 2006. “Extremely Randomized Trees.” Machine Learning 63 (1): 3–42. https://doi.org/10.1007/s10994-006-6226-1.
Gneiting, Tilmann, and Adrian E Raftery. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102 (477): 359–78. https://doi.org/10.1198/016214506000001437.
Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2015. “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation.” Journal of Computational and Graphical Statistics 24 (1): 44–65. https://doi.org/10.1080/10618600.2014.907095.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.
Gower, J. C. 1966. “Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis.” Biometrika 53 (3-4): 325–38. https://doi.org/10.1093/biomet/53.3-4.325.
Grinsztajn, Léo, Edouard Oyallon, and Gaël Varoquaux. 2022. “Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?” Advances in Neural Information Processing Systems 35: 507–20.
Hanley, J A, and B J McNeil. 1982. “The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve.” Radiology 143 (1): 29–36. https://doi.org/10.1148/radiology.143.1.7063747.
Hastie, Trevor, and Robert Tibshirani. 1986. “Generalized Additive Models.” Statistical Science 1 (3). https://doi.org/10.1214/ss/1177013604.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning. In Springer Series in Statistics. Springer New York. https://doi.org/10.1007/978-0-387-84858-7.
Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. Chapman; Hall/CRC. https://doi.org/10.1201/b18401.
He, Haibo, and E. A. Garcia. 2009. “Learning from Imbalanced Data.” IEEE Transactions on Knowledge and Data Engineering 21 (9): 1263–84. https://doi.org/10.1109/tkde.2008.239.
He, Jingyu, and P. Richard Hahn. 2023. “Stochastic Tree Ensembles for Regularized Nonlinear Regression.” Journal of the American Statistical Association 118 (541): 551–70. https://doi.org/10.1080/01621459.2021.1942012.
He, Jingyu, Saar Yalov, and P. Richard Hahn. 2019. “XBART: Accelerated Bayesian Additive Regression Trees.” Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, Proceedings of machine learning research, vol. 89: 1130–38.
Hechenbichler, Klaus, and Klaus Schliep. 2004. Weighted k-Nearest-Neighbor Techniques and Ordinal Classification. Discussion Paper No. 399. Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386.
Hernán, Miguel A., John Hsu, and Brian Healy. 2019. “A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks.” CHANCE 32 (1): 42–49. https://doi.org/10.1080/09332480.2019.1579578.
Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Chapman & Hall/CRC.
Hinton, G. E., and R. R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–7. https://doi.org/10.1126/science.1127647.
Hoerl, Arthur E., and Robert W. Kennard. 1970. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12 (1): 55–67. https://doi.org/10.1080/00401706.1970.10488634.
Hooker, Giles, Lucas Mentch, and Siyu Zhou. 2021. “Unrestricted Permutation Forces Extrapolation: Variable Importance Requires at Least One More Model, or There Is No Free Variable Importance.” Statistics and Computing 31 (6). https://doi.org/10.1007/s11222-021-10057-z.
Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. 1989. “Multilayer Feedforward Networks Are Universal Approximators.” Neural Networks 2 (5): 359–66. https://doi.org/10.1016/0893-6080(89)90020-8.
Hotelling, H. 1933. “Analysis of a Complex of Statistical Variables into Principal Components.” Journal of Educational Psychology 24 (6): 417–41. https://doi.org/10.1037/h0071325.
Hubert, Lawrence, and Phipps Arabie. 1985. “Comparing Partitions.” Journal of Classification 2 (1): 193–218. https://doi.org/10.1007/bf01908075.
Hyvärinen, A., and E. Oja. 2000. “Independent Component Analysis: Algorithms and Applications.” Neural Networks 13 (4-5): 411–30. https://doi.org/10.1016/s0893-6080(00)00026-5.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in r. In Springer Texts in Statistics. Springer US. https://doi.org/10.1007/978-1-0716-1418-1.
Jolliffe, Ian T., and Jorge Cadima. 2016. “Principal Component Analysis: A Review and Recent Developments.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374 (2065): 20150202. https://doi.org/10.1098/rsta.2015.0202.
Kaufman, Leonard, and Peter J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. In Wiley Series in Probability and Statistics. Wiley. https://doi.org/10.1002/9780470316801.
Kaufman, Shachar, Saharon Rosset, Claudia Perlich, and Ori Stitelman. 2012. “Leakage in Data Mining: Formulation, Detection, and Avoidance.” ACM Transactions on Knowledge Discovery from Data 6 (4): 1–21. https://doi.org/10.1145/2382577.2382579.
Ke, Guolin, Qi Meng, Thomas Finley, et al. 2017. “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” Advances in Neural Information Processing Systems 30: 3146–54.
King, Gary, and Langche Zeng. 2001. “Logistic Regression in Rare Events Data.” Political Analysis 9 (2): 137–63. https://doi.org/10.1093/oxfordjournals.pan.a004868.
Kingma, Diederik P., and Jimmy Ba. 2015. “Adam: A Method for Stochastic Optimization.” International Conference on Learning Representations. https://arxiv.org/abs/1412.6980.
Kingma, Diederik P., and Max Welling. 2014. “Auto-Encoding Variational Bayes.” International Conference on Learning Representations. https://arxiv.org/abs/1312.6114.
Kohavi, Ron. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1137–45.
Kuhn, Max, and Kjell Johnson. 2013. Applied Predictive Modeling. Springer New York. https://doi.org/10.1007/978-1-4614-6849-3.
Lawson, Charles L., and Richard J. Hanson. 1995. Solving Least Squares Problems. Society for Industrial; Applied Mathematics. https://doi.org/10.1137/1.9781611971217.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–44. https://doi.org/10.1038/nature14539.
Lee, Daniel D., and H. Sebastian Seung. 1999. “Learning the Parts of Objects by Non-Negative Matrix Factorization.” Nature 401 (6755): 788–91. https://doi.org/10.1038/44565.
Leek, Jeffery T., and Roger D. Peng. 2015. “What Is the Question?” Science 347 (6228): 1314–15. https://doi.org/10.1126/science.aaa6146.
Lei, Jing, Max G’Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and Larry Wasserman. 2018. “Distribution-Free Predictive Inference for Regression.” Journal of the American Statistical Association 113 (523): 1094–111. https://doi.org/10.1080/01621459.2017.1307116.
Little, M. A., P. E. McSharry, E. J. Hunter, J. Spielman, and L. O. Ramig. 2009. “Suitability of Dysphonia Measurements for Telemonitoring of Parkinson’s Disease.” IEEE Transactions on Biomedical Engineering 56 (4): 1015–22. https://doi.org/10.1109/tbme.2008.2005954.
Little, Roderick, and Donald Rubin. 2019. Statistical Analysis with Missing Data, Third Edition. In Wiley Series in Probability and Statistics. Wiley. https://doi.org/10.1002/9781119482260.
Lloyd, S. 1982. “Least Squares Quantization in PCM.” IEEE Transactions on Information Theory 28 (2): 129–37. https://doi.org/10.1109/tit.1982.1056489.
Luna, José Marcio, Efstathios D. Gennatas, Lyle H. Ungar, et al. 2019. “Building More Accurate Decision Trees with the Additive Tree.” Proceedings of the National Academy of Sciences 116 (40): 19887–93. https://doi.org/10.1073/pnas.1816748116.
Lundberg, Scott M., Gabriel Erion, Hugh Chen, et al. 2020. “From Local Explanations to Global Understanding with Explainable AI for Trees.” Nature Machine Intelligence 2 (1): 56–67. https://doi.org/10.1038/s42256-019-0138-9.
Lundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30: 4765–74.
MacQueen, James. 1967. “Some Methods for Classification and Analysis of Multivariate Observations.” Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1: 281–97.
Malley, J. D., J. Kruppa, A. Dasgupta, K. G. Malley, and A. Ziegler. 2012. “Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines.” Methods of Information in Medicine 51 (01): 74–81. https://doi.org/10.3414/me00-01-0052.
Martinetz, T. M., S. G. Berkovich, and K. J. Schulten. 1993. “‘Neural-Gas’ Network for Vector Quantization and Its Application to Time-Series Prediction.” IEEE Transactions on Neural Networks 4 (4): 558–69. https://doi.org/10.1109/72.238311.
McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models. Springer US. https://doi.org/10.1007/978-1-4899-3242-6.
McInnes, Leland, John Healy, and James Melville. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. https://arxiv.org/abs/1802.03426.
Meinshausen, Nicolai. 2006. “Quantile Regression Forests.” Journal of Machine Learning Research 7: 983–99.
Murphy, Allan H. 1973. “A New Vector Partition of the Probability Score.” Journal of Applied Meteorology 12 (4): 595–600. https://doi.org/10.1175/1520-0450(1973)0122.0.co;2.
Nelder, J. A., and R. W. M. Wedderburn. 1972. “Generalized Linear Models.” Journal of the Royal Statistical Society. Series A (General) 135 (3): 370. https://doi.org/10.2307/2344614.
Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. 2002. “On Spectral Clustering: Analysis and an Algorithm.” Advances in Neural Information Processing Systems 14: 849–56.
Niculescu-Mizil, Alexandru, and Rich Caruana. 2005. “Predicting Good Probabilities with Supervised Learning.” Proceedings of the 22nd International Conference on Machine Learning - ICML ’05, ICML ’05, 625–32. https://doi.org/10.1145/1102351.1102430.
Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. Cambridge University Press. https://doi.org/10.1017/cbo9780511803161.
Pearson, Karl. 1901. “On Lines and Planes of Closest Fit to Systems of Points in Space.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 2 (11): 559–72. https://doi.org/10.1080/14786440109462720.
Peduzzi, Peter, John Concato, Elizabeth Kemper, Theodore R. Holford, and Alvan R. Feinstein. 1996. “A Simulation Study of the Number of Events Per Variable in Logistic Regression Analysis.” Journal of Clinical Epidemiology 49 (12): 1373–79. https://doi.org/10.1016/s0895-4356(96)00236-3.
Peng, Roger D. 2011. “Reproducible Research in Computational Science.” Science 334 (6060): 1226–27. https://doi.org/10.1126/science.1213847.
Platt, John C. 1999. “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods.” In Advances in Large Margin Classifiers, edited by Alexander J. Smola, Peter Bartlett, Bernhard Schölkopf, and Dale Schuurmans. MIT Press.
Probst, Philipp, Marvin N. Wright, and Anne‐Laure Boulesteix. 2019. “Hyperparameters and Tuning Strategies for Random Forest.” WIREs Data Mining and Knowledge Discovery 9 (3). https://doi.org/10.1002/widm.1301.
Quinlan, J. R. 1986. “Induction of Decision Trees.” Machine Learning 1 (1): 81–106. https://doi.org/10.1007/bf00116251.
Riley, Richard D, Joie Ensor, Kym I E Snell, et al. 2020. “Calculating the Sample Size Required for Developing a Clinical Prediction Model.” BMJ, March, m441. https://doi.org/10.1136/bmj.m441.
Romano, Yaniv, Evan Patterson, and Emmanuel Candès. 2019. “Conformalized Quantile Regression.” Advances in Neural Information Processing Systems 32.
Romano, Yaniv, Matteo Sesia, and Emmanuel Candès. 2020. “Classification with Valid and Adaptive Coverage.” Advances in Neural Information Processing Systems 33: 3581–91.
Rousseeuw, Peter J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics 20 (November): 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.
Rubin, Donald B. 1976. “Inference and Missing Data.” Biometrika 63 (3): 581–92. https://doi.org/10.1093/biomet/63.3.581.
Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533–36. https://doi.org/10.1038/323533a0.
Sadinle, Mauricio, Jing Lei, and Larry Wasserman. 2019. “Least Ambiguous Set-Valued Classifiers with Bounded Error Levels.” Journal of the American Statistical Association 114 (525): 223–34. https://doi.org/10.1080/01621459.2017.1395341.
Saito, Takaya, and Marc Rehmsmeier. 2015. “The Precision-Recall Plot Is More Informative Than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets.” PLOS ONE 10 (3): e0118432. https://doi.org/10.1371/journal.pone.0118432.
Sandve, Geir Kjetil, Anton Nekrutenko, James Taylor, and Eivind Hovig. 2013. “Ten Simple Rules for Reproducible Computational Research.” PLoS Computational Biology 9 (10): e1003285. https://doi.org/10.1371/journal.pcbi.1003285.
Schölkopf, Bernhard, and Alexander J. Smola. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press. https://doi.org/10.7551/mitpress/4175.001.0001.
Shapley, Lloyd S. 1953. “A Value for n-Person Games.” In Contributions to the Theory of Games, Volume II, edited by Harold W. Kuhn and Albert W. Tucker. Annals of Mathematics Studies 28. Princeton University Press.
Shmueli, Galit. 2010. “To Explain or to Predict?” Statistical Science 25 (3): 289–310. https://doi.org/10.1214/10-sts330.
Srivastava, Nitish, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research 15 (56): 1929–58.
Stekhoven, Daniel J., and Peter Bühlmann. 2012. “MissForest—Non-Parametric Missing Value Imputation for Mixed-Type Data.” Bioinformatics 28 (1): 112–18. https://doi.org/10.1093/bioinformatics/btr597.
Steyerberg, E. W., and Y. Vergouwe. 2014. “Towards Better Clinical Prediction Models: Seven Steps for Development and an ABCD for Validation.” European Heart Journal 35 (29): 1925–31. https://doi.org/10.1093/eurheartj/ehu207.
Steyerberg, Ewout W. 2019. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. In Statistics for Biology and Health. Springer International Publishing. https://doi.org/10.1007/978-3-030-16399-0.
Stone, Charles J. 1977. “Consistent Nonparametric Regression.” The Annals of Statistics 5 (4). https://doi.org/10.1214/aos/1176343886.
Stone, M. 1974. “Cross-Validatory Choice and Assessment of Statistical Predictions.” Journal of the Royal Statistical Society Series B: Statistical Methodology 36 (2): 111–33. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x.
Strobl, Carolin, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn. 2007. “Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution.” BMC Bioinformatics 8 (1). https://doi.org/10.1186/1471-2105-8-25.
Štrumbelj, Erik, and Igor Kononenko. 2014. “Explaining Prediction Models and Individual Predictions with Feature Contributions.” Knowledge and Information Systems 41 (3): 647–65. https://doi.org/10.1007/s10115-013-0679-x.
Tenenbaum, Joshua B., Vin de Silva, and John C. Langford. 2000. “A Global Geometric Framework for Nonlinear Dimensionality Reduction.” Science 290 (5500): 2319–23. https://doi.org/10.1126/science.290.5500.2319.
Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society Series B: Statistical Methodology 58 (1): 267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Torgerson, Warren S. 1952. “Multidimensional Scaling: I. Theory and Method.” Psychometrika 17 (4): 401–19. https://doi.org/10.1007/bf02288916.
Tsanas, A., M. A. Little, P. E. McSharry, and L. O. Ramig. 2010. “Accurate Telemonitoring of Parkinson’s Disease Progression by Noninvasive Speech Tests.” IEEE Transactions on Biomedical Engineering 57 (4): 884–93. https://doi.org/10.1109/tbme.2009.2036000.
Tukey, John W. 1977. Exploratory Data Analysis. Addison-Wesley.
Valdes, Gilmer, Yannet Interian, Efstathios Gennatas, and Mark Van der Laan. 2022. “The Conditional Super Learner.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12): 10236–43. https://doi.org/10.1109/tpami.2021.3131976.
van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2011. “Mice: Multivariate Imputation by Chained Equations in r.” Journal of Statistical Software 45 (3). https://doi.org/10.18637/jss.v045.i03.
Van Calster, Ben, David J. McLernon, Maarten van Smeden, Laure Wynants, and Ewout W. Steyerberg. 2019. “Calibration: The Achilles Heel of Predictive Analytics.” BMC Medicine 17 (1): 230. https://doi.org/10.1186/s12916-019-1466-7.
van den Goorbergh, Ruben, Maarten van Smeden, Dirk Timmerman, and Ben Van Calster. 2022. “The Harm of Class Imbalance Corrections for Risk Prediction Models: Illustration and Simulation Using Logistic Regression.” Journal of the American Medical Informatics Association 29 (9): 1525–34. https://doi.org/10.1093/jamia/ocac093.
van der Laan, Mark J. 2017. “A Generally Efficient Targeted Minimum Loss Based Estimator Based on the Highly Adaptive Lasso.” The International Journal of Biostatistics 13 (2). https://doi.org/10.1515/ijb-2015-0097.
van der Laan, Mark J., and Katherine S. Pollard. 2003. “A New Algorithm for Hybrid Hierarchical Clustering with Visualization and the Bootstrap.” Journal of Statistical Planning and Inference 117 (2): 275–303. https://doi.org/10.1016/s0378-3758(02)00388-9.
van der Laan, Mark J., Eric C Polley, and Alan E. Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1). https://doi.org/10.2202/1544-6115.1309.
van der Maaten, Laurens, and Geoffrey Hinton. 2008. “Visualizing Data Using t-SNE.” Journal of Machine Learning Research 9: 2579–605.
Vapnik, Vladimir N. 1998. Statistical Learning Theory. Wiley.
Varma, Sudhir, and Richard Simon. 2006. “Bias in Error Estimation When Using Cross-Validation for Model Selection.” BMC Bioinformatics 7 (1). https://doi.org/10.1186/1471-2105-7-91.
Vickers, Andrew J., and Elena B. Elkin. 2006. “Decision Curve Analysis: A Novel Method for Evaluating Prediction Models.” Medical Decision Making 26 (6): 565–74. https://doi.org/10.1177/0272989x06295361.
von Luxburg, Ulrike. 2007. “A Tutorial on Spectral Clustering.” Statistics and Computing 17 (4): 395–416. https://doi.org/10.1007/s11222-007-9033-z.
Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer. 2005. Algorithmic Learning in a Random World. Springer. https://doi.org/10.1007/b106715.
Wahba, Grace. 1990. Spline Models for Observational Data. Society for Industrial; Applied Mathematics. https://doi.org/10.1137/1.9781611970128.
Westreich, D., and S. Greenland. 2013. “The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients.” American Journal of Epidemiology 177 (4): 292–98. https://doi.org/10.1093/aje/kws412.
Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software 59 (10). https://doi.org/10.18637/jss.v059.i10.
Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (1). https://doi.org/10.1038/sdata.2016.18.
Wolpert, David H. 1992. “Stacked Generalization.” Neural Networks 5 (2): 241–59. https://doi.org/10.1016/s0893-6080(05)80023-1.
Wolpert, David H. 1996. “The Lack of a Priori Distinctions Between Learning Algorithms.” Neural Computation 8 (7): 1341–90. https://doi.org/10.1162/neco.1996.8.7.1341.
Wood, Simon N. 2017. Generalized Additive Models: An Introduction with r. Chapman; Hall/CRC. https://doi.org/10.1201/9781315370279.
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.
Zadrozny, Bianca, and Charles Elkan. 2002. “Transforming Classifier Scores into Accurate Multiclass Probability Estimates.” Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD02, 694–99. https://doi.org/10.1145/775047.775151.
Zeileis, Achim, Torsten Hothorn, and Kurt Hornik. 2008. “Model-Based Recursive Partitioning.” Journal of Computational and Graphical Statistics 17 (2): 492–514. https://doi.org/10.1198/106186008x319331.
Zelnik-Manor, Lihi, and Pietro Perona. 2005. “Self-Tuning Spectral Clustering.” Advances in Neural Information Processing Systems 17: 1601–8.
Zou, Hui, and Trevor Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society Series B: Statistical Methodology 67 (2): 301–20. https://doi.org/10.1111/j.1467-9868.2005.00503.x.