What is the purpose of a learning curve (training size vs. error) when diagnosing a model?
Correct! Well done.
Incorrect.
The correct answer is B) It plots training and validation error as a function of training set size, helping diagnose whether a model suffers from high bias (both errors plateau high) or high variance (large gap between training and validation error)
Correct Answer
It plots training and validation error as a function of training set size, helping diagnose whether a model suffers from high bias (both errors plateau high) or high variance (large gap between training and validation error)
Learning curves reveal whether more data would help: a persistent gap between low training error and high validation error suggests overfitting (high variance), while both errors converging to a high value suggests the model is too simple (high bias) and needs more capacity or better features.