Q84 — AWS AIF-C01 Ch.3

Question 84 of 100 | ← Chapter 3

An AI specialist is training a regression ML model and observes bias and variance during training. Which bias-variance pattern indicates model overfitting?

• A. Low bias, low variance
• B. Low bias, high variance ✓
• C. High bias, low variance
• D. High bias, high variance

Correct Answer: B. Low bias, high variance

Explanation

In regression model training, bias and variance are key metrics for evaluating model performance. Bias reflects how well the model fits the training data, while variance reflects how consistently the model performs across different datasets. When model complexity is excessive, the model becomes overly flexible and fits training data very well—resulting in low bias—but performs poorly on unseen or test data due to sensitivity to small fluctuations—resulting in high variance. This low-bias, high-variance pattern typically indicates overfitting: the model memorizes noise and details in training data and lacks generalization capability. Thus, the low-bias, high-variance pattern leads to overfitting.