A flood-insurance screen was trained only on claims from one coastal county, then applied statewide, including inland river towns. Inland applications are denied at a much higher rate even when the stated loss facts match coastal ones. What problem does this describe?
Select an answer to reveal the explanation.
Short Explanation
Think of it like teaching a dog only on beach sand, then asking it to work a riverbank. The model learned one coastal county and then scored the whole state, so inland towns got a systematic no. That is bias from a skinny training set, not a noisy sensor or a dead GPU.
Full Explanation
Bias is a systematic skew that comes from unrepresentative or tainted examples. Training only on one coastal county and then scoring inland towns produces that skew. Random sensor noise is unstructured error, not a stable inland-versus-coastal pattern. A failed GPU is a hardware fault and does not encode a geographic preference. Clock speed is not a training example and is not how overfitting is defined.