if labels_train[ii] == 1 ] #### initial visualization plt.xlim(0.0, 1.0) plt.ylim(0.0, 1.0) plt.scatter(bumpy_fast, grade_fast, color="b", label="fast") plt.scatter(grade_slow, bumpy_slow, color="r", label="slow") plt.legend() plt.xlabel("bumpiness") plt.ylabel("grade") plt.show() ################################################################################ ### your code here! name your classifier object clf if you want the ### visualization code (prettyPicture) to show you the decision boundary from sklearn.neighbors import KDTree kdt = KDTree(features_train, leaf_size=30, metric='euclidean') t0 = time() kdt.query(features_train, k=2, return_distance=False) print("training time:", round(time() - t0, 3), "s") t0 = time() print(kdt.score(features_test)) try: # prettyPicture(clf, features_test, labels_test) prettyPicture(kdt, features_test, labels_test) except NameError: pass