[{'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:def extract_features(imgs, cspace='RGB', orient=9, pix_per_cell=8, cell_per_block=2, hog_channel=0):\nfeatures = []\nfor file in imgs:\nfile_features = []\nimage = mpimg.imread(file)\nif color_space != 'RGB':\nif color_space == 'HSV':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\nelif color_space == 'LUV':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)\nelif color_space == 'HLS':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\nelif color_space == 'YUV':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)\nelif color_space == 'YCrCb':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)\nelse: feature_image = np.copy(image)\nif hog_channel == 'ALL':\nhog_features = []\nfor channel in range(feature_image.shape[2]):\nhog_features.append(get_hog_features(feature_image[:,:,channel], orient,\npix_per_cell, cell_per_block, vis=False, feature_vec=True))\nhog_features = np.ravel(hog_features)\nelse:\nhog_features = get_hog_features(feature_image[:,:,hog_channel], orient,\npix_per_cell, cell_per_block, vis=False, feature_vec=True)\nfile_features.append(hog_features)\nfeatures.append(np.concatenate(file_features))\nreturn features"}, {'role': 'assistant', 'content': "['data_exploration', 'data_preprocessing', 'modelling']"}]