“2020 Retention Report.” 2020. Work Institute. https://doi.org/" ".
Aggarwal, Charu C. 2017. Outlier Analysis, Second Edition. SwitzerlandIndianapolis: Springer International Publishing.
Amelia. 2021. “A Program for Missing Data.” https://cran.r-project.org/web/packages/Amelia/Amelia.pdf.
Arnuld. 2020. “Some Key Things i Learned from Google’s " Introduction to Machine Learning Problem Framing" MOOC.” https://www.linkedin.com/pulse/some-key-things-i-learned-from-googles-introduction-machine-on-data.
Azen, Razia, and Nicole Traxel. 2009. “Using Dominance Analysis to Determine Predictor Importance in Logistic Regression.” Journal of Educational and Behavioral Statistics 34: 319–47.
Bramer, Max. 2007. Principles of Data Mining. New York: Springer.
Breiman, JH Friedman, L., and CJ Stone. 1984. Classification and Regression Trees. Boca Raton: Chapman; Hall/CRC.
Cady, Field. 2017. The Data Science Handbook. Hoboken, NJBoca Raton: John Wiley; Sons.
Cansiz, Sergen. 2020. “Mahalanobis Distance and Multivariate Outlier Detection in r.” https://towardsdatascience.com/mahalonobis-distance-and-outlier-detection-in-r-cb9c37576d7d.
ChristianSalas-Eljatiba, Timothy G. Gregoireb, AndresFuentes-Ramireza, and ValeskaYaitul. 2018. “A Study on the Effects of Unbalanced Data When Fitting Logistic Regression Models in Ecology.” Ecological Indicators, 502–8.
Clissold, Rachel. 2021. “How to Solve a Problem.” https://www.wikihow.com/Solve-a-Problem.
“CRISP-DM, Still the Top Methodology for Analytics, Data Mining, or Data Science Projects.” n.d. https://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html#:~:text=CRISP%2DDM%20remains%20the%20most,CRISP%2DDM%20is%20long%20overdue.
Davenport, Thomas H., and Jinho Kim. 2013. Keeping up with the Quants: Your Guide to Understanding and Using Analytics. Boston: Harvard Business Review Press.
DeRusha, Karen, and Bill WOlfson. n.d. “Shift Your Lens: The Power of Re-Framing Problems.” http://www.integratingengineering.org/workbook/documents/Problem.
Elkan, Charles. 2001. “The Foundations of Cost-Sensitive Learning.” In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=B24CDCB3FA2EBC3A4D5AEB2B35160B90?doi=10.1.1.29.514&rep=rep1&type=pdf.
Ellenberg, Jordan. 2016. “Abraham Wald and the Missing Bullet Holes.” https://medium.com/@penguinpress/an-excerpt-from-how-not-to-be-wrong-by-jordan-ellenberg-664e708cfc3d.
Flinchbaugh, Jamie. 2009. “Leading Lean: Solve the Right Problem.” https://www.assemblymag.com/articles/86658-leading-lean-solve-the-right-problem.
Gromping, U. 2019a. “South German Credit Data: Correcting a Widely Used Data Set.” http://www1.beuth-hochschule.de/FB_II/reports/Report-2019-004.pdf.
———. 2019b. “South German Credit (UPDATE) Data Set.” https://archive.ics.uci.edu/ml/datasets/South+German+Credit+.
Hauch, Brian. 2018. “Finding the True North of a Problem: Problem-Framing Principles for Design-Led Innovation.” https://medium.com/nyc-design/finding-the-true-north-of-a-problem-problem-framing-principles-for-design-led-innovation-b0c7620317bf.
“Hr—Analytics-Employee-Turnover.” n.d. Work Institute. https://doi.org/" ".
Jaspreet. 2016. “A Concise History of Neural Networks.” https://towardsdatascience.com/a-concise-history-of-neural-networks-2070655d3fec.
Jonassen, David. 1997. “Instructional Design Models for Well-Structured and Ill-Structured Problem-Solving Learning Outcomes.” Educational Technology Research and Development, 65–94.
Karimovich, Khamidov Sherzod Jaloldin ugli, Ganiev Salim, and Olimov Iskandar Salimbayevich. 2020a. “An Empirical Study of the Naïve Bayes Classifier.” In Proceedings of the 22nd International Conference on Machine Learning, 625–32.
———. 2020b. “Analysis of Machine Learning Methods for Filtering Spam Messages in Email Services.” In Proceedings of the 22nd International Conference on Machine Learning, 625–32.
“KDD and Data Mining.” n.d. https://www.datascience-pm.com/kdd-and-data-mining/.
Kuhn, Max, and Kjell Johnson. 2016. Applied Predictive Modeling. 2nd ed. New York: Springer.
Long, J. Scott, and Jeremy Frees. 2006. Regression Models for Categorical Dependent Variables Using Stata. 2nd ed. College Station, Texas: Chapman; Hall/CRC.
Ma, Kunihito Yamamori, Thae Ma, and Aye Thida. 2020. “A Comparative Approach to Naïve Bayes Classifier and Support Vector Machine for Email Spam Classification.” In IEEE 9th Global Conference on Consumer Electronics.
McCulloch, Warren S., and Walter H. Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Bulletin of Mathematical Biophysics, 114–33.
mice. 2021. “Multivariate Imputation by Chained Equations.” https://cran.r-project.org/web/packages/mice/mice.pdf.
Minsky, Marvin, and Seymour Papert. 1969. Perceptrons: An Introduction to Computational Geometry. Cambridge, Mass.: MIT Press.
mlr. 2021. “Machine Learning in r.” https://cran.r-project.org/web/packages/mlr/mlr.pdf.
mvoutlier. 2021. “Multivariate Outlier Detection Based on Robust Methods.” https://cran.r-project.org/web/packages/mvoutlier/mvoutlier.pdf.
Nielsen, Michael. 2019. “Neural Networks and Deep Learning.” http://neuralnetworksanddeeplearning.com/.
“Play Tennis: Simple Dataset with Decisions about Playing Tennis.” n.d. https://www.coursera.org/learn/machine-learning-under-the-hood.
Rubin, Donald B. 1976. “Inference and Missing Data.” Biometrika 63: 581–90.
Seelig, Tina. 2013. “Shift Your Lens: The Power of Re-Framing Problems.” http://stvp.stanford.edu/blog/?p=6435.
“SEMMA from SAS.” n.d. https://documentation.sas.com/doc/en/emref/14.3/n061bzurmej4j3n1jnj8bbjjm1a2.htm.
Shoemaker, Paul J. H., and J. Edward Russo. 2001. “Knitr: Manging Frames to Make Better Decisions.” In Wharton on Making Decisions, edited by Howard C. Kunreuther Hoch Stephen J. and Robert E. Gunther. John Wiley.
SMOTE. 2019. “A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE.” https://cran.r-project.org/web/packages/smotefamily/smotefamily.pdf.
“SMS Spam Collection Data Set, UCI Machine Learning Repository.” n.d. https://archive.ics.uci.edu/ml/datasets/sms+spam+collection.
“Spam Statistics and Facts.” n.d. https://www.spamlaws.com/spam-stats.html.
Taylor, James. 2017. “Bringing Business Clarity to CRISP-DM.” https://www.kdnuggets.com/2017/01/business-clarity-crisp-dm.html.
“The Crisp-DM User Guide.” n.d. https://s2.smu.edu/~mhd/8331f03/crisp.pdf.
“Top 10 Machine Learning Algorithms You Should Know in 2021.” n.d. https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-know-article.
Tukey, John. 1977. Exploratory Data Analysis. Addison-Wesley.
Unal, Hamit Taner, and Fatih Başçiftci. 2021. “Evolutionary Design of Neural Network Architectures: A Review of Three Decades of Research.” https://link.springer.com/article/10.1007/s10462-021-10049-5.
VIM. 2021. “Visualization and Imputation of Missing Values.” https://cran.r-project.org/web/packages/VIM/VIM.pdf.
“What Is TDSP?” n.d. https://www.datascience-pm.com/tdsp/.
Widmann, Maarit. n.d. “Cohen’s Kappa: What It Is, When to Use It, and How to Avoid Its Pitfalls.” https://thenewstack.io/cohens-kappa-what-it-is-when-to-use-it-and-how-to-avoid-its-pitfalls/#:~:text=Cohen's%20kappa%20is%20a%20metric,performance%20of%20a%20classification%20model.&text=Like%20many%20other%20evaluation%20metrics,based%20on%20the%20confusion%20matrix.
“Wolfram MathWord: Stirling Numbers of the Second Kind.” n.d. https://https://mathworld.wolfram.com/StirlingNumberoftheSecondKind.html.