Which penalty can land some coefficients exactly on zero?

• The squared penalty
• No penalty can do that
• The absolute penalty

What happens to the coefficients as the penalty grows?

• They grow without limit
• They shrink toward zero
• They freeze immediately

Features must be scaled before a penalty is fair.

Why must features be scaled before penalising?

• Scaling speeds up the computer clock
• Small numbers force big coefficients
• Scaling removes the need for any penalty on its own

Your regression fits training data perfectly with giant coefficients but flops on new data. What do you add, and what should you see?

• A penalty, with smaller coefficients and better new-data error
• More features, and even bigger coefficients
• A smaller training set, leaving the coefficients exactly as they were

You need automatic feature selection, not just smaller weights. Which penalty and why?

• Absolute, because exact zeros drop features out
• Squared, because smooth shrinking selects features
• Neither, because penalties cannot select

Someone penalises without scaling: one feature in kilometres dominates the penalty and gets crushed, though it predicts well. What went wrong?

• The penalty was too small to matter
• Units decided the charge, so a good feature was punished
• Absolute penalties always crush the features that predict well

After an absolute penalty, three of ten coefficients are exactly zero. A teammate says those three features were useless all along. What is the careful reading?

• They were useless, and the proof is final
• The penalty failed here, since zeros can only ever mean errors
• They added nothing given the rest, at this penalty strength