Pages that link to "Item:Q2172034"
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The following pages link to Actuarial intelligence in auto insurance: claim frequency modeling with driving behavior features and improved boosted trees (Q2172034):
Displaying 12 items.
- Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data (Q2066785) (← links)
- Imbalanced learning for insurance using modified loss functions in tree-based models (Q2172025) (← links)
- Autocalibration and Tweedie-dominance for insurance pricing with machine learning (Q2665871) (← links)
- BERT-based NLP techniques for classification and severity modeling in basic warranty data study (Q2682975) (← links)
- BAYESIAN ANALYSIS OF BIG DATA IN INSURANCE PREDICTIVE MODELING USING DISTRIBUTED COMPUTING (Q4563820) (← links)
- COST-SENSITIVE MULTI-CLASS ADABOOST FOR UNDERSTANDING DRIVING BEHAVIOR BASED ON TELEMATICS (Q5019037) (← links)
- JOINT MODELING OF CLAIM FREQUENCIES AND BEHAVIORAL SIGNALS IN MOTOR INSURANCE (Q5067880) (← links)
- Claims frequency modeling using telematics car driving data (Q5743535) (← links)
- A Markov-modulated tree-based gradient boosting model for auto-insurance risk premium pricing (Q5858899) (← links)
- Pay-As-You-Drive Insurance: Modeling and Implications (Q6110494) (← links)
- Bayesian CART models for insurance claims frequency (Q6152709) (← links)
- Enhanced gradient boosting for zero-inflated insurance claims and comparative analysis of <tt>CatBoost</tt> , <tt>XGBoost</tt> , and <tt>LightGBM</tt> (Q6656764) (← links)
