[{'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:test_clusters = range(5,16,1)\ntest_n_components = [10,15,20]\nnormalizer = Normalizer(copy=False)\nbest_score = 0\nfor n_components in test_n_components:\ntime0 = time()\nsvd = TruncatedSVD(n_components)\nlsa = make_pipeline(svd, normalizer)\nX = lsa.fit_transform(tfidf)\nelapsed_lsa = time() - time0\nfor clusters in test_clusters:\ntime0 = time()\nkm = MiniBatchKMeans(n_clusters=clusters)\nkm.fit(X)\nelapsed_km = time() - time0\nscore = metrics.silhouette_score(X, km.labels_, sample_size=1000)\nif score > best_score:\nbest_score = score\nbest_n_components = n_components\nbest_n_clusters = clusters\nbest_svd = svd\nbest_km = km\nbest_X = X\nprint("Components: %.3d, clusters: %.2d, silhouette score: %.4f (%d seconds)"\n% (n_components, clusters, score, elapsed_lsa+elapsed_km))\nprint()\nprint("\\nBest number of components: %i\\nBest number of clusters: %i\\nAchieved silhouette score: %.4f"\n% (best_n_components, best_n_clusters, best_score))\nprint()'}, {'role': 'assistant', 'content': "['evaluation', 'modelling']"}]