print('***********') #DODAVANJE X i Y x = iris.data y = iris.target.reshape(-1, 1) #column vector #y = iris.target.reshape(1,-1) #row vector print(x.shape, y.shape) print('***********') #TRAIN TEST x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42, stratify=y) print(x_train.shape, y_train.shape) print('***********') print(x_test.shape, y_test.shape) print('***********') #FITOVANJE from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=5, p=2) knn.fit(x_train, y_train.ravel()) #PRIKAZ REZULTATA list_res = [] for p in [1, 2]: knn.p = p for k in range(1, 10, 2): knn.n_neighbors = k y_pred = knn.predict(x_test) acc = accuracy_score(y_test, y_pred)*100 list_res.append([k, 'l1_distance' if p == 1 else 'l2_distance', acc]) df1 = pd.DataFrame(list_res, columns=['k', 'dist. func.', 'accuracy']) print(df1) print('***********')
plot_learning_curve=False) # Compare different leaf sizes clfs_leaf_size = dict() for leaf_size in range(5, 71, 10): clf = copy.deepcopy(base) clf.leaf_size = leaf_size clfs_leaf_size['{}-leaf_size'.format(leaf_size)] = clf compare_models_all_metrics(clfs_leaf_size, x_cr, y_cr, train_sizes=train_sizes, title_prefix="Credit Fraud", plot_learning_curve=False) # Compare different p values clfs_p = dict() for p in range(2, 35, 5): clf = copy.deepcopy(base) clf.p = p clfs_p['{}-p'.format(p)] = clf compare_models_all_metrics(clfs_p, x_cr, y_cr, train_sizes=train_sizes, title_prefix="Credit Fraud", plot_learning_curve=False) print("Booty - Done")