[{'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:t=time.time()\ncolor_space = \'YCrCb\'  # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\norient = 9  # HOG orientation angles\npix_per_cell = 8\ncell_per_block = 2\nhog_channel = "ALL"  # aka 1st feature channel. 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, spatial_size=spatial_size,\nhist_bins=hist_bins, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block,\nhog_channel=hog_channel,\nspatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)\nnotcar_features = extract_features(notcars, color_space=color_space, spatial_size=spatial_size,\nhist_bins=hist_bins, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block,\nhog_channel=hog_channel,\nspatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)\nprint(time.time() - t, \'Seconds to compute features\')\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=0.8, random_state=rand_state)\nsvc = LinearSVC()\nt = time.time()\nsvc.fit(X_train, y_train)\nprint(round(time.time() - t, 2), \'Seconds to train SVC\')'}, {'role': 'assistant', 'content': "['modelling']"}]