[{'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 operator\ndef explore_models():\naccuracies = []\nvectorizer = TfidfVectorizer(min_df=1, stop_words=\'english\')\ntrain_vectors = vectorizer.fit_transform(train_text)\nprint("train_vectors.shape:", train_vectors.shape)\ndev_vectors = vectorizer.transform(dev_text)\nprint("dev_vectors.shape:", dev_vectors.shape)\nprint\nprint("------------------------------")\nprint("K Nearest Neighbors (K-NN)")\nprint("------------------------------")\nks = [1, 5, 15, 16, 17, 18, 19, 20, 28, 29, 30, 31, 32, 150, 300]\nf1_scores = []\nfor k in ks:\nknn = KNeighborsClassifier(n_neighbors=k, distance=\'cosine\', algorithm=\'brute\')\nknn.fit(train_vectors, train_labels)\npred_1 = knn.predict(dev_vectors)\nprint("K-NN: f1_score = %s, k = %s" %(round(metrics.f1_score(dev_labels, pred_1, average=\'binary\'),4), k))\nf1_scores.append(metrics.f1_score(dev_labels, pred_1))\nprint\nresults_knn = dict(zip(ks, f1_scores))\nprint("K-NN: optimal k =", max(results_knn.items(), key=operator.itemgetter(1))[0])#if you are using python 2, change "items()" to iteritems()\nprint\nprint("-----------------------------")\nprint("Bernoulli Naive Bayes (BNB)")\nprint("-----------------------------")\nalphas = [0.0, 0.00001, 0.0001, 0.001, 0.01, 0.094, 0.095, 0.096, 0.1, 0.105, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0, 10.0]\nf1_scores = []\nfor a in alphas:\nbnb = BernoulliNB(alpha=a,binarize=0.5)\nbnb.fit(train_vectors, train_labels)\npred_2 = bnb.predict(dev_vectors)\nprint("BNB: f1_score = %s, alpha = %s" % (round(metrics.f1_score(dev_labels, pred_2, average=\'binary\'), 4), a))\nf1_scores.append(metrics.f1_score(dev_labels, pred_2))\nprint\nresults_bnb = dict(zip(alphas, f1_scores))\nprint("Bernoulli Naive Bayes: optimal alpha =", max(results_bnb.items(), key=operator.itemgetter(1))[0])\nprint\nprint("------------------------")\nprint("Logistic Regression (LR)")\nprint("------------------------")\nprint\ncs = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, \\\n10, 12, 20, 30, 40, 50, 100, 1000]\nf1_scores = []\nfor c in cs:\nlr = LogisticRegression(penalty=\'l2\',C=c)\nlr.fit(train_vectors, train_labels)\npred_3 = lr.predict(dev_vectors)\nprint("-------------------------------")\nprint("LR: f1_score = %s, C = %s" % (round(metrics.f1_score(dev_labels, pred_3, average=\'binary\'),4), c))\nprint("-------------------------------")\nf1_scores.append(metrics.f1_score(dev_labels, pred_3, average=\'binary\'))\naccuracies.append((lr.score(dev_vectors, dev_labels))*100)\nfun_sq_wts = lambda x: x**2\nsq_wts = map(fun_sq_wts, lr.coef_[0])\nsum_sq_wts =  sum(sq_wts)\nprint("Label = 1, sum of squared weights = %s" % (sum_sq_wts))\nprint\nresults_lr = dict(zip(cs, f1_scores))\nprint("Logistic Regression: optimal C =", max(results_lr.items(), key=operator.itemgetter(1))[0])\nprint\nprint("max accuracy =", max(accuracies))\nexplore_models()'}, {'role': 'assistant', 'content': 'modelling'}]