[{'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)\ncolor_space = \'HSV\' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\norient = 9  # HOG orientations\npix_per_cell = 8 # HOG pixels per cell\ncell_per_block = 4 # HOG cells per block\nhog_channel = 1 # Can be 0, 1, 2, or "ALL"\nspatial_size = (32, 32) # Spatial binning dimensions\nhist_bins = 32    # Number of histogram bins\nspatial_feat = True # Spatial features on or off\nhist_feat = True # Histogram features on or off\nhog_feat = True # HOG features on or off\ncar_features = extract_features(cars, color_space=color_space,\nspatial_size=spatial_size, hist_bins=hist_bins,\norient=orient, pix_per_cell=pix_per_cell,\ncell_per_block=cell_per_block,\nhog_channel=hog_channel, spatial_feat=spatial_feat,\nhist_feat=hist_feat, hog_feat=hog_feat)\nnotcar_features = extract_features(notcars, color_space=color_space,\nspatial_size=spatial_size, hist_bins=hist_bins,\norient=orient, pix_per_cell=pix_per_cell,\ncell_per_block=cell_per_block,\nhog_channel=hog_channel, spatial_feat=spatial_feat,\nhist_feat=hist_feat, hog_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:\',orient,\'orientations\',pix_per_cell,\n\'pixels per cell and\', cell_per_block,\'cells per block\')\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()'}, {'role': 'assistant', 'content': 'modelling'}]