首页|Southwest Jiaotong University Reports Findings in Machine Learning (A hybrid app roach for modeling bicycle crash frequencies: Integrating random forest based SH AP model with random parameter negative binomial regression model)
Southwest Jiaotong University Reports Findings in Machine Learning (A hybrid app roach for modeling bicycle crash frequencies: Integrating random forest based SH AP model with random parameter negative binomial regression model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news originating from Sichuan, People’s Republic of China, by NewsRx correspondents, research stated, “To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley Additive ex Planations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB). First, four machine learning algorithms, including random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. ”
SichuanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRisk and Prevention
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