Description
I have a question about hyperparameter tuning: how were the values for alpha and other tunable parameters chosen for the passion, gamma (frequency*severity) and tweedie models? Did you do something like grid search and cross validation?
I also thought that it would be nice to have a method to calculate the D-squared score for the composite frequency*severity model, so that it can be compared to the Tweedie model ( The Tweedie GridSearchCV chooses the best value for power based on D-squared score in the example).
Another question is since there's discussion about Gini index at the end of the example, would it make sense to also use Gini index as one of the scoring metrics in GridSearchCV? In insurance applications, if coming up with most accurate rates is the goal, maybe MAE/RMSE is suitable, while Gini index is better for the purpose of ranking policy holders in terms of risk.
Last suggestion is that a function for deriving the relativities of features would be really useful.
Thank you!