XGBoost-in-Insurance-2017
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Data and Code to reproduce results for my talk at Paris: R in Insurance 2017 Conference
XGBoost in Insurance 2017
Data and Code to reproduce results for my talk at Paris: R in Insurance 2017 Conference
Non life pricing: empirical comparison of classical GLM with tree based Gradient Boosted Models
To reproduce the results, please:
- clone the repository
- set your working directory to the cloned depository
- Inside the
code folder
run:-
00_init_*
to initiate the workspace correctly. -
02_train_*
to execute parameter tuning and save xgboost models. -
03_scoring
to execute cross-validation and obtain results, on both GAM and xgboost.
-
Further, 01_preprocess_*.R
code to prepare the dataset is provided.
Note: xgboost mdoels are not provided directly since they exceed the size limit of GitHub. Feel free to reach out and I will provide them privately if pre-tuning is too expensive.
Thank you!