accuracy = accuracy_score(labels_test, pred) print accuracy ####### K Nearest Neighbors ##### ### # ### # ###################################################################################################### from sklearn.neighbors import KNeighborsClassifier t0 = time() clf = KNeighborsClassifier(n_neighbors=1) # default value is 2 # clf = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(features_train) clf.fit(features_train, labels_train) print "training time:", round(time()-t0, 3), "s" distances, indices = clf.kneighbors(features_train) t1 = time() pred = clf.predict(features_test) print "predict time:", round(time()-t1, 3), "s" # # Method 1 from sklearn.metrics import accuracy_score accuracy = accuracy_score(labels_test, pred) print accuracy ############ AdaBoost ########### ### # ### # ###################################################################################################### from sklearn.cross_validation import cross_val_score
