- (i) False. Lasso reduces the number of variables, thus is less flexible.
- (ii) False. Same justification as in (i).
- (iii) True.
- (iv) False. In general, lasso reduces variance and increases bias. Reduction in variance should compensate increasement in bias.
Same answer as above.
- (i) False. In general, non-linear methods reduce bias and increase variance. Reduction in bias should be compensate increasement in variance.
- (ii) True.
- (iii) False. Non-linear methods are more flexible because they accomodate to the data. Unlike least squares, non-linear methods don't assume a parametrized relationship between the predictors and the response.
- (iv) False. Same justification as in (iii).