References

Aizerman, Mark A, Emmanuel M Braverman, and LI Rozoner. 1964. “Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning.” Automation and Remote Control 25: 821–37.

Allaire, JJ, Joe Cheng, Yihui Xie, Jonathan McPherson, Winston Chang, Jeff Allen, Hadley Wickham, Aron Atkins, Rob Hyndman, and Ruben Arslan. 2018. Rmarkdown: Dynamic Documents for R. Chapman; Hall/CRC.

Bellman, Richard E. 1966. “Dynamic Programming.” Science 153 (3731): 34–37.

Bergstra, James, and Yoshua Bengio. 2012. “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research 13 (Feb): 281–305.

Boser, Bernhard E, Isabelle M Guyon, and Vladimir N Vapnik. 1992. “A Training Algorithm for Optimal Margin Classifiers.” In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–52. COLT ’92. New York, NY, USA: ACM.

Breiman, Leo. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–40.

———. 2001. “Random Forests.” Machine Learning 45 (January): 5–32.

Breiman, Leo, JH Friedman, R Olshen, and CJ Stone. 1984. “Classification and Regression Trees.”

Chen, Tianqi, and Carlos Guestrin. 2016. “Xgboost: A Scalable Tree Boosting System.” In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–94. ACM.

Cortes, Corinna, and Vladimir Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–97.

Drucker, Harris, Christopher JC Burges, Linda Kaufman, Alex J Smola, and Vladimir Vapnik. 1997. “Support Vector Regression Machines.” In Advances in Neural Information Processing Systems, 155–61.

Fader, Peter S, Bruce GS Hardie, and Ka Lok Lee. 2005. “RFM and Clv: Using Iso-Value Curves for Customer Base Analysis.” Journal of Marketing Research 42 (4): 415–30.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. The Elements of Statistical Learning. Vol. 1. 10. Springer series in statistics New York, NY, USA.

Goddard, Philip. 2017. “Machine Learning Pipelines with Scikit-learn.”

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.

Hahnloser, Richard HR, Rahul Sarpeshkar, Misha A Mahowald, Rodney J Douglas, and H Sebastian Seung. 2000. “Digital Selection and Analogue Amplification Coexist in a Cortex-Inspired Silicon Circuit.” Nature 405 (6789): 947.

Hastie, Trevor, and Junyang Qian. 2014. “Glmnet Vignette.” Retrieve from Http://Www. Web. Stanford. Edu/~ Hastie/Papers/Glmnet_Vignette.pdf. Accessed 30.05.2019 20: 2016.

Heckman, James J. 1976. “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models.” In Annals of Economic and Social Measurement, Volume 5, Number 4, 475–92. NBER.

Hughes, AM. 1996. “Boosting Response with Rfm.” Marketing Tools 5 (January): 4–7.

Hughes, Gordon. 1968. “On the Mean Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14 (1): 55–63.

Hunter, JD. 2007. “Matplotlib: A 2D Graphics Environment.” Computing in Science Engineering 9 (3): 90–95.

Kearns, Michael. 1988. “Thoughts on Hypothesis Boosting.” Unpublished Manuscript 45: 105.

Kluyver, Thomas, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, et al. 2016. “Jupyter Notebooks – a Publishing Format for Reproducible Computational Workflows.” Edited by F Loizides and B Schmidt. IOS Press.

Kohavi, Ron, and others. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” In Ijcai, 14:1137–45. 2. Montreal, Canada.

Kohavi, Ron, and Rajesh Parekh. 2004. “Visualizing Rfm Segmentation.” In Proceedings of the 2004 Siam International Conference on Data Mining, 391–99. SIAM.

Kursa, Miron B, and Witold Rudnicki. 2011. “The All Relevant Feature Selection Using Random Forest.” arXiv Preprint arXiv:1106.5112, June.

Kursa, Miron B, Witold R Rudnicki, and others. 2010. “Feature Selection with the Boruta Package.” Journal of Statistical Software 36 (11): 1–13.

Lemaître, Guillaume, Fernando Nogueira, and Christos K Aridas. 2017. “Imbalanced-Learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning.” Journal of Machine Learning Research 18 (17): 1–5.

McCarty, John A, and Manoj Hastak. 2007. “Segmentation Approaches in Data-Mining: A Comparison of Rfm, Chaid, and Logistic Regression.” Journal of Business Research 60 (6): 656–62.

McKinney, Wes. 2010. “Data Structures for Statistical Computing in Python.” In Proceedings of the 9th Python in Science Conference, edited by Stéfan van der Walt and Jarrod Millman, 51–56.

———. 2011. “Pandas: A Foundational Python Library for Data Analysis and Statistics.” Python for High Performance and Scientific Computing 14.

Oliphant, Travis E. 2006. A Guide to Numpy. Vol. 1. Trelgol Publishing USA.

Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30.

Rubin, Donald B. 1976. “Inference and Missing Data.” Biometrika 63 (3): 581–92.

Stubseid, Saavi, and Ognjen Arandjelovic. 2018. “Machine Learning Based Prediction of Consumer Purchasing Decisions: The Evidence and Its Significance.” In AAAI Conference on Artificial Intelligence.

Tipping, Michael E. 2001. “Sparse Bayesian Learning and the Relevance Vector Machine.” Journal of Machine Learning Research 1 (Jun): 211–44.

Troyanskaya, Olga, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, and Russ B Altman. 2001. “Missing Value Estimation Methods for Dna Microarrays.” Bioinformatics 17 (6): 520–25.

van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2011. “Mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software, Articles 45 (3): 1–67.

Waskom, Michael, O Botvinnik, P Hobson, J Warmenhoven, JB Cole, Y Halchenko, J Vanderplas, et al. 2014. “Seaborn: Statistical Data Visualization.” Seaborn: Statistical Data Visualization Seaborn 0 5.

Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Chapman and Hall/CRC.

———. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Chapman and Hall/CRC.

Yeh, I-Cheng, King-Jang Yang, and Tao-Ming" Ting. 2009. “Knowledge Discovery on Rfm Model Using Bernoulli Sequence.” Expert Systems with Applications 36 (3, Part 2): 5866–71.

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.