A data scientist observes that increasing training set size improves validation accuracy but training accuracy stays fixed near 100%. This learning curve pattern is characteristic of which condition?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because training accuracy fixed at 100% while validation accuracy improves with more data is the signature of high variance. The model is memorizing training data; more data helps validation catch up.
Full explanation below image
Full Explanation
B is correct because training accuracy fixed at 100% while validation accuracy improves with more data is the signature of high variance. The model is memorizing training data; more data helps validation catch up. A (high bias) would show both training and validation error plateauing at a high value. C would show both curves converging at a low value. D (label noise) would cause training accuracy to degrade, not stay at 100%.