# References

Breiman, Leo. 2001. “Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author).” Statistical Science 16 (3): 199–231. https://doi.org/10.1214/ss/1009213726.

Domingos, Pedro. 2012. “A Few Useful Things to Know About Machine Learning.” Commun. ACM 55 (10). https://doi.org/10.1145/2347736.2347755.

Efron, Bradley, and Trevor Hastie. 2016. Computer Age Statistical Inference. Vol. 5. Cambridge University Press.

Fernández-Delgado, Manuel, Eva Cernadas, Senén Barro, and Dinani Amorim. 2014. “Do We Need Hundreds of Classifiers to Solve Real World Classification Problems.” J. Mach. Learn. Res 15 (1): 3133–81.

Harrell, F. 2015. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer Series in Statistics. Springer International Publishing.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd ed. 2017. Corr. 12th Printing. Springer.

Kuhn, Max, and Kjell Johnson. 2013. Applied Predictive Modeling. Vol. 26. Springer.

Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press.

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “Why Should I Trust You?: Explaining the Predictions of Any Classifier.” In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 1135–44. ACM.

Wood, S. N. n.d. Generalized Additive Models: An Introduction with R. Vol. 66. CRC Press.