Gradient boosting trees for auto insurance loss cost modeling and prediction

By Leo Guelman. 2012

238 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yufei Xia
2. Chuanzhe Liu
3. Yuying Li
4. Nana Liu

In Statistics and Probability

1. Yufei Xia
2. Chuanzhe Liu
3. Yuying Li
4. Nana Liu

Citations in the corpus

• A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, background): “Recent studies show that boosting technique is a promising approach in the domain of insurance loss modeling (Guelman, 2012), travel time prediction (Yanru Zhang & Haghani, 2015), and chemistry (Brillante et al.”