Bias, Variance, and Learning Curves
Test your understanding of how model fit changes with complexity and data, plus what learning curves reveal.
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Quiz Questions & Answers
Review every prompt, the correct responses, and helpful context to prep for your own run-through.
Question 1: What usually signals high bias in a model?
It underfits both training and test data
It only fails on the test set
It memorizes the training set very well
Its predictions are always perfectly stable
Question 2: What is the best description of high variance?
Training and test error are both equally high
The model changes a lot with different training data
It always improves as the model gets simpler
The model is too simple to learn anything useful
Question 3: A model has low training error but much higher test error. What is the most likely issue?
Overfitting from high variance
Underfitting from high bias
Perfect generalization
A data leak that always lowers test error
Question 4: On a learning curve, what does a high training error that stays high suggest?
The model has memorized the labels perfectly
The test set is probably too large
The model is likely too biased
The model is likely overfitting
Question 5: When both training and validation error are high and close together, what is the best interpretation?
More training data will always hurt performance
High bias is the main problem
High variance is the main problem
The model is definitely too complex
Question 6: What often happens to variance when you make a model more complex?
Variance always disappears
Variance and bias both always fall together
Variance tends to increase
Training error must rise sharply
Question 7: What is a common effect of adding more training data for a high-variance model?
It guarantees lower training error
It makes the model ignore the data entirely
It can reduce overfitting
It always increases bias dramatically
Question 8: If a learning curve shows a large gap between training and validation error, what should you suspect first?
High bias
High variance
A perfect balance of bias and variance
A bug that only affects the training set size