A model's learning curve shows that training error and validation error have both converged to a high value (approximately 0.35 RMSE) and neither decreases as more training data is added. What does this indicate?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — b is correct because when both training and validation errors converge to a similar high value and adding more data does not help, the model suffers from high bias — it lacks the capacity to capture patterns in the data. The remedy is to increase model complexity (add features, use a more powerful algorithm, or add polynomial terms).
Full Explanation
B is correct because when both training and validation errors converge to a similar high value and adding more data does not help, the model suffers from high bias — it lacks the capacity to capture patterns in the data. The remedy is to increase model complexity (add features, use a more powerful algorithm, or add polynomial terms). A (overfitting) would show a large gap between training error (low) and validation error (high). C is wrong because high, stable error is not calibration success. D is wrong because reducing data size would worsen a high-bias model.