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Found 4 papers, 1 papers with code
Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM
The property and casualty (P&C) insurance industry faces challenges in developing claim predictive models due to the highly right-skewed distribution of positive claims with excess zeros.
The SAMME.C2 algorithm for severely imbalanced multi-class classification
Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories.
Synthetic Dataset Generation of Driver Telematics
This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset.
Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics
In this article, we explore how to integrate telematics information to better predict claims frequency.
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