[{'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:#voting <br>from sklearn.ensemble import VotingClassifier<br># from sklearn.model_selection import GridSearchCV<br><br>ens_clf=VotingClassifier(estimators=[(\'kn\', knn), (\'ml\', MLP),(\'gbc\', GBC_clf), (\'rf\', random_forest_clf), <br>                                     (\'dt\', DT_clf), (\'et\', ET_clf), (\'adb\', AdB_clf)],<br>                                      voting=\'soft\', weights=[1, 2, 3, 5, 5, 5, 4])<br><br><br># grid = GridSearchCV(estimator=ens_clf,  cv=5)<br><br>ens_clf.fit(train_normalized,train_labels)<br><br>#ens_acc = cross_val_score(ens_clf, train_normalized, train_labels, cv=10, scoring=\'accuracy\')<br><br><br>trainpredEns=ens_clf.predict(train_normalized)<br>#print(metrics.accuracy_score(train_labels, trainpredEns))<br><br># print (ens_acc)<br># print(np.mean(ens_acc))<br><br>print("ENSDone")'}, {'role': 'assistant', 'content': "['modelling']"}]