Non life pricing empirical comparison of classical GLM

Non Life Pricing Empirical Comparison Of Classical Glm-PDF Download

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1 Methodologies and Tools,GAM A better GLM,Tree Based Gradient Boosted Models. 2 Empirical Results,Adopted Datasets,Performance Results. Leonardo Petrini Non life pricing empirical comparison of classical GLM with. Paris tree,June Gradient,2017 Boosted,Motivation Why should we bother. A correct and accurate pricing,A better understanding of the risk components. Number of Claims NB and Claim Severity CS,Key Quantity.
BurningCost NB CS, Leonardo Petrini Non life pricing empirical comparison of classical GLM with. Paris tree,June Gradient,2017 Boosted,Beyond GLM Generalised Additive Models. g E y x 0 f1 x1 fp xp,Advantages,Effective in treating non linearity. Can adapt to a large variety of scenarios,Disadvantages. Can easily lead to overfitting,Computationally intensive.
The mgcv package,Define a formula,Create a parallel cluster. Run the mgcv bam, Leonardo Petrini Non life pricing empirical comparison of classical GLM with. Paris tree,June Gradient,2017 Boosted,Beyond GLM Sample R code. library mgcv,library parallel,ctrl list nthreads,cl makeCluster. gamNB bam formula data family,stopCluster cl, Leonardo Petrini Non life pricing empirical comparison of classical GLM with.
Paris tree,June Gradient,2017 Boosted,Gradient Boosted Models Understanding the Hype. Decision Tree based models,Proven to work in Insurance. XGBoost The Kaggle to go model,Actively used by companies as. Leonardo Petrini Non life pricing empirical comparison of classical GLM with. Paris tree,June Gradient,2017 Boosted,eXtreme Gradient Boosting The State of Art. Ensemble of Decision Trees,Boosting Algorithm,Active community.
Computationally attractive,10x Faster than GBM, Leonardo Petrini Non life pricing empirical comparison of classical GLM with. Paris tree,June Gradient,2017 Boosted,XGBoost Sample R code. library xgboost,train xgb DMatrix data label,test xgb DMatrix data label. watchlist list train test,model xgb train params list. data nround rounds eta,objective eval metric, Leonardo Petrini Non life pricing empirical comparison of classical GLM with.
Paris tree,June Gradient,2017 Boosted,Adopted Datasets. CAS Dataset freMTPL,Private Dataset Actuarial Pricing Game. Pre Processing,Cross Validation,Metrics Used,Number of Claims Poisson Log Loss. Claim Severity Root Mean Square Error,Burning Cost Normalised Gini Index. Leonardo Petrini Non life pricing empirical comparison of classical GLM with. Paris tree,June Gradient,2017 Boosted,CAS Dataset GAM vs XGBoost.
LogLoss RMSE Gini,GAM 0 16 3852 0 24,XGB 0 17 1980 0 30. XGB Tweedie 0 25, Leonardo Petrini Non life pricing empirical comparison of classical GLM Paris. Gradient Boosted,Private Dataset GAM vs XGBoost,LogLoss RMSE Gini. GAM 0 38 2530 0 35,XGB 0 39 990 0 44,XGB Tweedie 0 38. Leonardo Petrini Non life pricing empirical comparison of classical GLM Paris. Gradient Boosted, leopetrini mail com GitHub XGBoost in Insurance 2017.
Leonardo Petrini Non life pricing empirical comparison of classical GLM Paris. Non life pricing empirical comparison of classical GLM with tree based Gradient Boosted Models Innovative approach to pure premium estimation Leonardo Petrini Paris 8th June 2017 Leonardo Petrini Non life pricing empirical comparison of classical GLM with tree based Gradient Boosted ModelsParis 8th June 2017 1 12 Outline 1 Methodologies and Tools GAM A better GLM Tree Based Gradient

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