[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:sample_size = 1200\ncars = cars[0:sample_size]\nnotcars = notcars[0:sample_size]\ncar_features = extract_features(cars, cspace=color_space, orient=orient, pix_per_cell=pix_per_cell,\ncell_per_block=cell_per_block, hog_channel=hog_channel)\nnotcar_features = extract_features(notcars, cspace=color_space, orient=orient, pix_per_cell=pix_per_cell,\ncell_per_block=cell_per_block, hog_channel=hog_channel)\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(scaled_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]))"}, {'role': 'assistant', 'content': "['data_exploration', 'modelling']"}]