Quiz
Regularization and the Bias-Variance Trade-off
Understanding how L2 regularization strength affects bias and variance in a high-variance model.
You train a linear regression model with L2 (ridge) regularization and observe low training error but much higher validation error. As you increase the regularization strength (lambda) from a very small value, which outcome best describes the expected effect on bias and variance?