the results are shown in figure - in[ ]from sklearn datasets import load_breast_cancer cancer load_breast_cancer(x_trainx_testy_trainy_test train_test_splitcancer datacancer targetstratify=cancer targetrandom_state= training_accuracy [test_accuracy [try n_neighbors from to neighbors_settings range( for n_neighbors in neighbors_settingsbuild the model clf kneighborsclassifier(n_neighbors=n_neighborsclf fit(x_trainy_trainrecord training set accuracy training_accuracy append(clf score(x_trainy_train)record generalization accuracy test_accuracy append(clf score(x_testy_test)plt plot(neighbors_settingstraining_accuracylabel="training accuracy"plt plot(neighbors_settingstest_accuracylabel="test accuracy"plt ylabel("accuracy"plt xlabel("n_neighbors"plt legend(the plot shows the training and test set accuracy on the -axis against the setting of n_neighbors on the -axis while real-world plots are rarely very smoothwe can still recognize some of the characteristics of overfitting and underfitting (note that because considering fewer neighbors corresponds to more complex modelthe plot is horizontally flipped relative to the illustration in figure - considering single nearest neighborthe prediction on the training set is perfect but when more neighbors are consideredthe model becomes simpler and the training accuracy drops the test set accuracy for using single neighbor is lower than when using more neighborsindicating that using the single nearest neighbor leads to model that is too complex on the other handwhen considering neighborsthe model is too simple and performance is even worse the best performance is somewhere in the middleusing around six neighbors stillit is good to keep the scale of the plot in mind the worst performance is around accuracywhich might still be acceptable supervised machine learning algorithms