Câu 2: PEGACPDS25V1: Certified Pega Data Scientist

As a data scientist, you can improve the predictive power of adaptive models by adding predictors to the models. The models automatically learn which predictors are valuable by capturing the responses. Regarding this adaptive learning process, what is the key difference between parameterized predictors and non-parameterized predictors?

1. A. Unlike non-parameterized predictors, when parameterized predictors are highly correlated, they are not grouped by the system.
2. B. The number of parameterized predictors is limited, while the number of non-parameterized predictors is unlimited.
3. C. Only non-parameterized predictors influence propensity.
4. D. There is no key difference between parameterized predictors and non-parameterized predictors. — đáp án hiện tại