The best split using entropy would be 0.6 - 0.4- entropy 0.2 - 0.00 0.25 0.50 0.75 1.00 p O a. p=0 O b. p=0.5 O c. p=0.25 O d. p=0.9 O e. none of theseOf these plots of the olive oils data, which is the best summary 8400 - 100 - ... 8000 - 75- pleic arachidic 7600 - 50 25 7200 - He.. .... . .. 500 750 1000 1250 1500 500 750 1000 1250 1500 linoleic linoleic O a. trees would use multiple splits on arachidic but not linoleic to get a good model for separating the groups O b. arachidic in conjunction with linoleic provides a big difference between the two groups but its more difficult to model O c. none of these O d. linoleic gives the best split for the two groups O e. QDA would provide a useful quadratic boundary between these groups, because the variances are different and ellipticalFor this data, which of these would be the best fit split? X=1, 3, 4, 5, 7; y=A, B, A, B, B O a. 4.5 O b. 3 O c. 2 O d. 6 O e. 1When the variables are highly correlated, and the difference between groups uses a combination of variables. Trees will perform than LDA. O a. about the same 0 b. better 0 c. none of these 0 d. very differently 0 e. worse What is feature engineering? O a. Collecting new variables O b. Creating new variables from existing variables O c. A way to take linear combinations of variables for dimension reeduction O d. A type of model for prediction O e. None of these