(Machine Learning) Use SVM from sklearn to classify non-linearly sperable datasets. Refer to the example in sklearn http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html, you can use this code or part of it in your solutions. Load (using load_breast_cancer) datasets from sklearn (datasets.load_breast_cancer()):

a. select and evalute the "best kernal SVM" and the "worse kernel SVM" model could fit this dataset (you can empirically select the hyperparameters values), justify your answer.

b. report the cross validation accuracy for the "best kernel SVM" and the "worse kernel SVM" classifier.

c. Using random search, what is the optimimum hyperparameter values for the "optimum model" ?