[{'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:def classify_train(clf_type, X_train, train_category):\nif clf_type == "logreg": #81.1\nlogreg = linear_model.LogisticRegression(C=8.25, max_iter=3000, tol=1e-8)\nlogreg.fit(X_train, train_category)\nreturn logreg\nelif clf_type == "svm_rbf": #80.3\nclf = svm.SVC(kernel=\'rbf\', gamma=0.0001, C=1, decision_function_shape="ovr", probability=True)\nclf.fit(X_train, train_category)\nreturn clf\nelif clf_type == "svm_linear":\nclf = svm.SVC(kernel = \'linear\', probability = True)\nclf.fit(X_train, train_category)\nreturn clf\nelif clf_type == "sgd": #76.9\nclf = linear_model.SGDClassifier(n_iter=2000,loss = \'modified_huber\', penalty = \'elasticnet\', n_jobs=-1)\nclf.fit(X_train,train_category)\nreturn clf\nelif clf_type == "nb":\nclf = MultinomialNB()\nclf.fit(X_train,train_category)\nreturn clf\nelif clf_type == "nn":\nclf = MLPClassifier(solver=\'adam\', alpha=1e-5,hidden_layer_sizes=(500,150), max_iter=50000, random_state=1)\nclf.fit(X_train,train_category)\nreturn clf\nelif clf_type == "ensemble":\nclf1 = linear_model.LogisticRegression(C=8.25, max_iter=3000, tol=1e-8)\nclf3 = svm.SVC(kernel=\'rbf\', gamma=0.0001, C=1, decision_function_shape="ovr",probability=True)\nclf4 = linear_model.SGDClassifier(n_iter=2000,loss = \'modified_huber\', penalty = \'elasticnet\', n_jobs=-1)\neclf = VotingClassifier(estimators=[(\'lr\',clf1),(\'svm_rbf\',clf3), (\'sgd\' , clf4)], voting="soft")\neclf = eclf.fit(X_train,train_category)\nreturn eclf'}, {'role': 'assistant', 'content': "['modelling']"}]