Use the following image as ar refernce to do steps below Step $2$: $($Compare six SVM classifiers with polynomial kernel $($kernel$=$'poly'$),$ degrees $[3,5,7],$ and gamma values $[\mathrm{.}3,\mathrm{.}5]$ by drawing a grid of six plots similar to the one you obtained from step $1.$ Step $3$: Compare the following six classifiers by drawing a grid of six plots similar to the one you obtained from step $1$:

$$ linear regression

$$ linearSVC

$$ GaussianNB w$/$ var$\_$smoothing$=2$e$1$

$$ GaussianNB w$/$ var$\_$smoothing$=1$e$1$

$$ GaussianNB w$/$ var$\_$smoothing$=1$e$0$

$$ GaussianNB w$/$ var$\_$smoothing$=1$e$-1$

Step $4$: Compare six variations of SVM classifiers with kernels $[\text{'}$sigmoid$\text{'},\text{'}$rfb$\text{'}],$ and gamma values $[1$e$-1,1$e$0,1$e$2]$ by drawing a grid of six plots similar to the one you obtained from step $1.$

Step $5$: Compare eight variations of neural network MLP classifiers with default alpha $(1$e$-4),$ solvers $[\text{'}$adam$\text{'},\text{'}$lbfgs$\text{'}],$ activation $[\text{'}$logistic$\text{'},$ 'relu'$]$ and layers $[(30,30),(10,5)]$ by drawing a grid of eight plots similar to the one you obtained from step $1.$Step $1$: use this code below to draw the following six scatter plots depicting the way different

classifiers perform on the iris dataset which has $50$ samples for each of its three classes$($labels$).$

These plots figure each class with a different color $($red$,$ blue, white$)$

#Step $1$: loading the dataset

iris $=$ datasets.load$\_$iris$0$

#Step $2$: transforming the dataset features to reduce dimensions from $4D$ to $2D$

X$\_$transformed $=$ pca.fit$\_$transform$($iris$.$data$)$

#Step $3$: obtaining the true labels of dataset

$y=$ iris.target

#Step $4$: splitting the dataset into $15\%$ testing set and $85\%$ training set in a random fashion

X$\_$train, X$\_$test, $y_{?}{?}_{\text{t}\text{r}\text{a}\text{i}\text{n}\text{,}}y\_$test $=$ train$\_$test$\_$split $,$ test$\_$size $=0\mathrm{.}15,$ random$\_$state $=23)$

#Step $4$: Defining $5$ models and fitting them to our training set

models

LinearRegression$(),$

svm$.$LinearSVC $,$ max$\_$iter$=10000),$

svm$.$SVC$($kernel$=$'poly', degree$=3,$ gamma$=\mathrm{.}1,$ C$=1),$

MLPClassifier$($solver$=\text{'}$lbfgs$\text{'},$ alpha$=\mathrm{.}5,$ hidden$\_$layer$\_$sizes$=(2,2),$ activation$=$'logistic',

random$\_$state $=23),$

GaussianNB$($var$\_$smoothing$=1$e$1),$

svm$.$SVC$($kernel$=$"sigmoid", gamma$=0\mathrm{.}6,$ C$=1),$

# Step $5$: Drawing the plots

titles $=("$Linear regression",

"LinearSVC",

$"$SVC W$/$ poly$3\mathrm{.}1",$

$"$NN layer:$(2,2)",$

"GaussianNB",

$"$SVC w$/$ Sig$\mathrm{.}6")$

fig, sub $=$ plt$.$subplots $(2,3)$

plt$.$subplots$\_$adjust$(($wspace $=0\mathrm{.}4,$ hspace $=0\mathrm{.}7)$

$x0,x1=x\_$train$[$:$,0],$ X$\_$train :$,1$

for clf$,$ title, ax in zip$($models$,$ titles, sub.flatten$(0)$:

disp $=$ DecisionBoundaryDisplay.from$\_$estimator$($