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 algo = 'KNN - n_100 e w_dist' print(algo) from sklearn.neighbors import KNeighborsClassifier clf = KNeighborsClassifier(n_neighbors=100, weights='distance') clf.fit(features_train, labels_train) print(clf.score(features_test, labels_test)) try: prettyPicture(clf, features_test, labels_test, algo) except NameError: pass # %%
import numpy as np import pylab as pl features_train, labels_train, features_test, labels_test = makeTerrainData() ########################## SVM ################################# ### we handle the import statement and SVC creation for you here from sklearn import tree clf = tree.DecisionTreeClassifier() clf.fit(features_train, labels_train) print(clf.score(features_test, labels_test)) #### now your job is to fit the classifier #### using the training features/labels, and to #### make a set of predictions on the test data prettyPicture(clf, features_test, labels_test, 'DT') #### store your predictions in a list named pred pred = clf.predict(features_test) from sklearn.metrics import accuracy_score acc = accuracy_score(pred, labels_test) def submitAccuracy(): return acc # %% # %%