During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required. When looking to gather data, what approach is best when determining how much data you need?

During CPMAI Phase II (Data Understanding) teams follow the “Goldilocks” principle—acquire an amount of training data that is “just right.” Too little causes under-fitting and bias; too much inflates costs and latency. Teams iteratively sample, profile, and test to hit the optimal balance for model performance and project constraints.

B – Insufficient data jeopardizes statistical validity. C – Excessive data wastes resources and may degrade training speed without added value. D – CPMAI explicitly prescribes the Goldilocks guideline; therefore a correct approach exists.

1. Cognilytica. CPMAI v7 Guide, §3.3 “Goldilocks Approach to Data Sizing,” pp. 35-36.

2. University of Washington CSE 547 Lecture Notes, “Dataset Size vs. Performance,” 2021, slides 7-8.

3. Domingos, P. “A Few Useful Things to Know about Machine Learning,” Communications of the ACM, 55(10), 2012, p. 82.