Exercise 6.2#

(a)#

  • (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.

(b)#

Same answer as above.

(c)#

  • (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).