Q$4.(25$ pts$).$ Consider the following $10-$record Iris dataset.

$\backslash$table$[[\backslash$table$[[$Sepal$],[$Length$]],\backslash$table$[[$Sepal$],[$Breadth$]],\backslash$table$[[$Petal$],[$Length$]],\backslash$table$[[$Petal$],[$Breadth$]],$Species$],[6\mathrm{.}8,3\mathrm{.}2,5\mathrm{.}9,2\mathrm{.}3,$Iris$-$virginica$],[6\mathrm{.}9,3\mathrm{.}1,5\mathrm{.}1,2\mathrm{.}3,$Iris$-$virginica$],[4\mathrm{.}9,3,1\mathrm{.}4,0\mathrm{.}2,$Iris$-$setosa$],[5\mathrm{.}6,3,4\mathrm{.}5,1\mathrm{.}5,$Iris$-$versicolor$],[4\mathrm{.}8,3\mathrm{.}1,1\mathrm{.}6,0\mathrm{.}2,$Iris$-$setosa$],[5\mathrm{.}8,2\mathrm{.}8,5\mathrm{.}1,2\mathrm{.}4,$Iris$-$virginica$],[7\mathrm{.}2,3\mathrm{.}6,6\mathrm{.}1,2\mathrm{.}5,$Iris$-$virginica$],[5\mathrm{.}1,3\mathrm{.}5,1\mathrm{.}4,0\mathrm{.}3,$Iris$-$setosa$],[4\mathrm{.}7,3\mathrm{.}2,1\mathrm{.}6,0\mathrm{.}2,$Iris$-$setosa$],[6\mathrm{.}6,3,4\mathrm{.}4,1\mathrm{.}4,$Iris$-$versicolor$]]$

Use logistic regression to fit three linear decision boundaries that separate one class of flowers from the other two categories.

Use these decision boundaries to predict the class for flowers with the following dimensions:

$\backslash$table$[[,$Sepal Length,Sepal Breadth,Petal Length,Petal Breadth$],[($a$)3\mathrm{.}0,2\mathrm{.}0,1\mathrm{.}0,0\mathrm{.}5,],[($b$),2\mathrm{.}0,3\mathrm{.}0,3\mathrm{.}0,1\mathrm{.}5],[($c$),4\mathrm{.}0,2\mathrm{.}5,4\mathrm{.}5,1\mathrm{.}0],[($d$),6\mathrm{.}0,4\mathrm{.}0,5\mathrm{.}5,2\mathrm{.}2]]$