Question

What can be learned from a predictive model should not change if the _________ is either included or excluded in the training set

A: Dataset filter
B: Individual's data
C: Model fairness score
D: Biased dataset

Answer 1

The presence or absence of a single individual's data should not significantly change what can be learned from a predictive model, indicating the model's robustness and reliable predictive accuracy.

Step-by-step explanation:

The correct answer is B: Individual's data. The integrity and predictive accuracy of a model should not drastically change when a single individual's data is either included or excluded from the training set. Predictive models are designed to understand patterns and make predictions based on large datasets. If the omission or inclusion of a single individual significantly affects the output, it suggests that the model is too sensitive to small variances and might not generalize well to new data. It is important to have a model that is both robust and reliable, which means it can handle the variance inherent in real-world data without its performance being compromised.