[{'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:# kf = KFold(n_splits=5,shuffle=True)\n\n# random_forest_acc = 0\n# adaboost_acc = 0\n# extraRandom_acc = 0\n# svm_acc = 0\n# gradientBoosting_acc = 0 \n\n# for train_index, test_index in kf.split(data):\n\n#     X_train = data.filter(items=train_index, axis=0)\n#     X_test = data.filter(items=test_index, axis=0)\n    \n#     y_train = input_label[train_index]\n#     y_test = input_label[test_index]\n\n# #   for randomForest  \n#     random_forest_clf = RandomForestClassifier(n_estimators=50)\n#     random_forest_clf.fit(X_train, y_train)\n#     rand_given_labels = random_forest_clf.predict(X_test)\n#     random_forest_acc += accuracy_score(y_test, rand_given_labels)\n    \n# #   for AdaBoost  \n#     adaboost_clf = AdaBoostClassifier(n_estimators = 100,learning_rate=0.5)\n#     adaboost_clf.fit(X_train, y_train)\n#     ada_given_labels = adaboost_clf.predict(X_test)\n#     adaboost_acc += accuracy_score(y_test, ada_given_labels)\n# #   for extra random forest  \n#     extraRandom= ExtraTreesClassifier(n_estimators=100, max_depth=None,min_samples_split=2)\n#     extraRandom.fit(X_train, y_train)\n#     xrand_given_labels = extraRandom.predict(X_test)\n#     extraRandom_acc += accuracy_score(y_test, xrand_given_labels)    \n# #   for gradient boosting\n#     gradientBoosting_clf = GradientBoostingClassifier(n_estimators=350, learning_rate=.1,max_depth=1)\n#     gradientBoosting_clf.fit(X_train, y_train)\n#     gradientBoosting_given_labels = gradientBoosting_clf.predict(X_test)\n#     gradientBoosting_acc += accuracy_score(y_test, gradientBoosting_given_labels)    \n# #   for svm\n#     svm_clf = svm.SVC(C= 0.1 , kernel='linear')\n#     svm_clf.fit(X_train, y_train)\n#     svm_given_labels = svm_clf.predict(X_test)\n#     svm_acc += accuracy_score(y_test, svm_given_labels)    \n    "}, {'role': 'assistant', 'content': 'helper_functions'}]