Per Dan:
Build in variable importance function that uses:
built in functions with sci-kit learn
Shapley Value based importance (run-time would be 2^n (number of models to fit) where n is the number of predictors/features in the model)
Perhaps we could use correlation to make a network so that instead of testing all coalitions, we only test those with high correlation
The assumption would be that the contribution of independent variables woud be roughly additive. (this seems fair)
We would still look at all possible subsets, but for uncorrelated variables, we could just add up their contributions
If Shaply Value importance is fit on training and evaluated on holdout, then after we calculate Shapley we could just remove all variables with a negative shapley value
This would be an alternative to forward/backward regression for variable selection
Figure out a way to evaluate variable importance when using dummy variables
Per Dan:
Build in variable importance function that uses:
built in functions with sci-kit learn
Shapley Value based importance (run-time would be 2^n (number of models to fit) where n is the number of predictors/features in the model)
Perhaps we could use correlation to make a network so that instead of testing all coalitions, we only test those with high correlation
The assumption would be that the contribution of independent variables woud be roughly additive. (this seems fair)
We would still look at all possible subsets, but for uncorrelated variables, we could just add up their contributions
If Shaply Value importance is fit on training and evaluated on holdout, then after we calculate Shapley we could just remove all variables with a negative shapley value
This would be an alternative to forward/backward regression for variable selection