fig plt figure (ax fig gcaprojection ' 'ax scatter ( [bidx , [bidx , [bidx , =' 'marker ='^'label ='benign 'ax scatter ( [midx , [midx , [midx , =' 'marker =' 'label ='malignant 'ax legend (ax set_xlabel ('mean radius 'ax set_ylabel ('mean texture 'ax set_zlabel ('mean concavity 'plt show (benign mean co ncavity malignant mea ra dius ea ur xt te figure scatterplot of three features of the benign and malignant breast masses the following code uses various classifiers to predict the category of breast masses (benign or malignantin this case the training set has elements and the test set has elements for each classifier the percentage of correct predictions (that isthe accuracyin the test set is reported we see that in this case quadratic discriminant analysis gives the highest accuracy ( exercise explores the question whether this metric is the most appropriate for these data skclass py from skclass import x_train y_train x_test y_test from sklearn metrics import accuracy_score import sklearn discriminant_analysis as da from sklearn naive_bayes import gaussiannb from sklearn neighbors import kneighborsclassifier from sklearn linear_model import logisticregression from sklearn svm import svc names [logit ",nbayes ""lda""qda""knn""svm"