Predictive Insights AI · Book
Contents
  1. Introduction
  2. Ch 0 · DataFest
  3. 1.1 Data exploration
  4. 1.2 Intro to modeling
  5. 1.3 GLMs
  6. 1.4 GLMs for classification
  7. 1.5 Classification metrics
  8. 1.6 More GLM topics
  9. 1.7 Variable selection
  10. 1.8 Tree-based models
  11. 1.9 Unsupervised learning
  12. Ch 2 · Cybersecurity
  13. 3.1 Health costs case study
  14. 3.2 Apartment applicants
  15. 3.3 Titanic case study
  16. Ch 4 · Tools
  17. Ch 5 · Seven mistakes
  18. Ch 6 · Entrepreneurship
  19. Wrap-up
  20. Appendix
  21. References
  22. PA R Study Manual

Predictive Analytics & Risk Modeling: For Insurance and Actuarial Science

References

Free sample

References and acknowledgements.

Burkov, Andriy. 2019. The Hundred-Page Machine Learning Book. https://themlbook.com/

Goldburd, Mark et al. 2016.Generalized Linear Models for Insurance Rating: CAS Monograph Series Number 5. Print.

Hastie, Trevor, et al. 2002. The Elements of Statistical Learning. Print.

James, Gareth, et al. 2017. An Introduction to Statistical Learning. https://www.statlearning.com/

Rigollet, Philippe (2017). Lecture 21: Generalized Linear Models. Video. https://www.youtube.com/watch?v=X-ix97pw0xY&t=899s

Wickham, Hadley. 2019. R for Data Science. https://r4ds.had.co.nz/

Society of Actuaries Past Exams, 2021. https://www.soa.org/education/exam-req/edu-exam-pa-detail

These examples of the Ridge and Lasso are an adaptation of p. 251-255 of “Introduction to Statistical Learning with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Adapted by R. Jordan Crouser at Smith College for SDS293: Machine Learning (Spring 2016), and re-implemented in Fall 2016 in tidyverse format by Amelia McNamara and R. Jordan Crouser at Smith College.

Used with permission from Jordan Crouser at Smith College. Additional Thanks to the following contributors on github:

Sources and further reading

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