IITM MLT Week 6 Questions
Ridge Regression and Model Selection
Week 6 question PDF
Review how regularization changes regression coefficients and model performance. The collection includes training and validation errors, ridge regression, Bayesian interpretations and bias-variance questions.
The complete question statements, options, numerical data and any supporting graphs or tables are in the PDF. Numbered comprehension questions can include several subquestions. Answer keys are included for checking your attempt; these are not full worked solutions.
Topics covered in this collection
- Ridge regression and regularization
- Bias, variance and model complexity
- Cross-validation and hyperparameters
- Bayesian linear regression
How to approach these questions
- Use validation performance to choose the regularization strength rather than minimizing training error alone.
- Compare how changing model complexity affects bias and variance instead of assuming test error always increases.
- Connect a Gaussian prior on the weights with the quadratic penalty in ridge regression.
Attempt the questions using the assumptions and notation stated in each problem. For short-answer questions, round only at the final step to the requested precision. Check the provided key after solving and revisit the relevant concept when an answer differs.