[{'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:names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",\n         "Decision Tree", "Random Forest", "Neural Net", "AdaBoost",\n         "Naive Bayes", "QDA"]\n\nclassifiers = [\n    KNeighborsClassifier(3),\n    SVC(kernel="linear", C=0.025),\n    SVC(gamma=2, C=1),\n    GaussianProcessClassifier(1.0 * RBF(1.0)),\n    DecisionTreeClassifier(max_depth=5),\n    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n    MLPClassifier(alpha=1),\n    AdaBoostClassifier(),\n    GaussianNB(),\n    QuadraticDiscriminantAnalysis()]\n\nres = []\nres2 = []\nfor name, clf in zip(names, classifiers):\n    clf.fit(inputf, labels)\n    res.append(clf.predict(testf))\n    temp = clf.predict(inputf)\n    res2.append(temp)\n    print(name, " : ", sklearn.metrics.accuracy_score(labels, temp))\n\n# res.append(res1)'}, {'role': 'assistant', 'content': 'modelling'}]