Lasso and Elastic Net Regression Quiz

Answers and explanations appear as you take the quiz.

1. A lasso fit assigns an exactly zero coefficient to a feature. What does that indicate within this fitted model?
   • The feature is excluded from its linear prediction
   • The feature has no association in every reasonable model
   • The feature was removed during data preprocessing
   • The feature has no variation in the source population
2. Which penalty combination defines elastic net in the guide?
   • L1 penalty with alpha denoting only the L1 share
   • L1 plus L2
   • L1 penalty only, with no L2 component
   • L2 penalty with L1 used only during feature scaling
3. Two highly correlated predictors alternate as the one retained by lasso across resamples. What does this show?
   • Standardization guarantees stable selection whenever predictors are correlated
   • Any change in selected features proves the test set leaked into cross-validation
   • Their selection can be unstable despite similar predictions
   • Lasso is guaranteed to assign identical coefficients across resamples
4. Why should scaling be learned inside each cross-validation training fold?
   • Fit the scaler once on all rows, then split for cross-validation
   • Fit scaling parameters on validation rows only
   • Scale after selecting the final penalty using test data
   • To keep validation-fold information out of preprocessing
5. In the common mixing-ratio convention, which endpoint corresponds to pure lasso?
   • The endpoint with all L2 penalty
   • The endpoint with all L1 penalty
   • The endpoint with no penalty
   • The setting that removes the intercept
6. A dataset has predictors measured in dollars and in tiny fractions. Why can scaling matter for penalized regression?
   • Feature scaling cannot affect penalty behavior when units change
   • Rescaling the target is the only preprocessing relevant to coefficients
   • The penalty acts on coefficient magnitudes, which depend on feature units
   • Scaling after fitting leaves the selected coefficients unchanged
7. When should a final test set be used if penalty settings are chosen by cross-validation?
   • Only if training error is zero
   • Repeatedly to choose each penalty value
   • Before fitting the preprocessing pipeline
   • After model selection, for a final evaluation
8. Which statement about a zero lasso coefficient is appropriately cautious?
   • It is a result of this penalized fit, not universal proof of no relationship
   • It proves this predictor has no causal effect
   • It will remain zero across populations and penalty settings
   • It establishes that no nonlinear relationship exists