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 and Infographic
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 below image
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.