Câu 6: PEGACPDS25V1: Certified Pega Data Scientist

A global insurance company is implementing Pega Customer Decision Hub and wants to maximize the predictive accuracy of their models for determining customer propensity to purchase additional coverage. The data science team is evaluating different modeling techniques. What is the key difference between Gradient Boosting models and Bayesian models in Adaptive Decision Manager that would influence the choice between them?

1. A. Gradient boosting models typically offer higher predictive accuracy but with lower transparency compared to Bayesian models. — đáp án hiện tại
2. B. Gradient boosting models can only be used for numeric predictors, while Bayesian models work with both numeric and symbolic predictors.
3. C. Gradient boosting models have significantly longer runtime compared to Bayesian models, making them impractical for real-time decisions.
4. D. Bayesian models can only be used for web channel predictions, while Gradient boosting models work across all channels.