[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:images = glob.glob('training_sample/*.jpeg')\ncars = []\nnotcars = []\nfor image in images:\nif 'image' in image or 'extra' in image:\nnotcars.append(image)\nelse:\ncars.append(image)\nspatial = 32\nhistbin = 32\nspatial_size   = (spatial,spatial)\ncolor_space    = 'YCrCb'\norient         = 9\npix_per_cell   = 8\ncell_per_block = 4\nhog_channel    = 'ALL'\nspatial_feat   = False\nhist_feat      = False\nhog_feat       = True\ncar_features = extract_features(cars, color_space, spatial_size,\nhistbin,\norient,\npix_per_cell, cell_per_block, hog_channel,\nspatial_feat, hist_feat, hog_feat)\nnotcar_features = extract_features(notcars, color_space, spatial_size,\nhistbin,\norient,\npix_per_cell, cell_per_block, hog_channel,\nspatial_feat, hist_feat, hog_feat)\nX = np.vstack((car_features, notcar_features)).astype(np.float64)\nX_scaler = StandardScaler().fit(X)\nscaled_X = X_scaler.transform(X)\ny = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\nrand_state = np.random.randint(0, 100)\nX_train, X_test, y_train, y_test = train_test_split(\nscaled_X, y, test_size=0.2, random_state=rand_state)\nprint('Using spatial binning of:',spatial,\n'and', histbin,'histogram bins')\nprint('Feature vector length:', len(X_train[0]))\nsvc = LinearSVC()\nt=time.time()\nsvc.fit(X_train, y_train)\nt2 = time.time()\nprint(round(t2-t, 2), 'Seconds to train SVC...')\nprint('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\nt=time.time()\nn_predict = 10\nprint('My SVC predicts: ', svc.predict(X_test[0:n_predict]))\nprint('For these',n_predict, 'labels: ', y_test[0:n_predict])\nt2 = time.time()\nprint(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')"}, {'role': 'assistant', 'content': 'modelling'}]