[{'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:import numpy as np\nimport cv2\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport pickle\nimport random\nimport glob\nimport time\nfrom sklearn.svm import LinearSVC\nfrom sklearn.preprocessing import StandardScaler\nfrom skimage.feature import hog\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle\n%matplotlib inline\ncolor_space = \'RGB\' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb, GRAY\norient = 30  # HOG orientations\npix_per_cell = 16 # HOG pixels per cell\ncell_per_block = 2 # HOG cells per block\nhog_channel = \'ALL\' # Can be 0, 1, 2, or "ALL"\nspatial_size = (16, 16) # Spatial binning dimensions\nhist_bins = 16 # Number of histogram bins\nspatial_feat = False # Spatial features on or off\nhist_feat = False # Histogram features on or off\nhog_feat = True # HOG features on or off\ny_start_stop = [400, 720] # Min and max in y to search in slide_window()\nx_start_stop = [0, 1280] # ditto for x\npct_overlap = 0.7 # sliding window overlap percentage\nheatmap_thresh = 33\nnum_frames = 30 # number of video frames over which to accumulate heatmap\nmin_ar, max_ar = 0.7, 3.0 # bounding box acceptable aspect ratio range\nsmall_bbox_area, close_y_thresh = 80*80, 500\nmin_bbox_area = 40*40'}, {'role': 'assistant', 'content': 'helper_functions'}]