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License: Creative Commons Attribution 4.0 International
Explainable Machine Learning Approaches for Shock Lapse Modeling
License: Creative Commons Attribution 4.0 International
This issue tracks writing the code to produce the plots.
Build the SOA2015 model as specified, to the best of our ability. This will be used as the state of the art baseline.
Describe process going from raw data from SOA to modeling dataset.
We don't need to go into too much detail (out of scope) but we may want to show these and briefly describe what they are.
Describe how we tried to replicate the SOA2015 model.
The SOA likes to break links, so I'm keeping track of sources here
2010 experience study: https://www.soa.org/research-reports/2010/research-shock-lapse-report/
The dataset provided has age as a factor, with bands as levels. The 2014 report https://www.soa.org/experience-studies/2014/research-2014-post-level-shock/ has average values for each band, so we can use those.
SOA2015 actually uses duration (in the form (Duration - 9)^(-n) for n = 1, 2, 3) as a predictor and claims that
This model also extends beyond the initial shock lapse all the way to duration 19
The dataset they provided doesn't really support this, as
> table(insurance::lapse_study$duration)
10 11 12 13+ 6-9
59730 29659 19233 35069 201936
we'll do our best here and take 10, 11, and 12, and convert those to numeric
Describe and justify the weighted RMSE measure and how the train/test split is done.
We should do an initial training/testing split on a policy year boundary. Maybe 80/20. For the evaluation metric, weighted RMSE (with exposure as weights) should be reasonable.
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