[{'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:from sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.model_selection import cross_validate\n\n# returns model instance\ndef get_model(): \n    params = {\n        "n_estimators":300, \n        "n_jobs": 3,\n        "random_state":5436,\n    }\n    return ExtraTreesClassifier(**params)\n\n# validate model on given dataset and report CV score\ndef validate_model(model, data):\n    skf = StratifiedKFold(n_splits=5, random_state = 4141, shuffle = True)\n    stats = cross_validate(\n        model, data[0], data[1], \n        groups=None, scoring=\'roc_auc\', \n        cv=skf, n_jobs=None, return_train_score = True\n    )\n    stats = pd.DataFrame(stats)\n    return stats.describe().transpose()\n\n# transforms given train and test datasets using provided function, \n# function parameters can be passed as a dict\ndef transform_dataset(train, test, func, func_params = {}):\n    dataset = pd.concat([train, test], ignore_index = True)\n    dataset = func(dataset, **func_params)\n    if isinstance(dataset, pd.DataFrame):\n        new_train = dataset.iloc[:train.shape[0],:].reset_index(drop = True)\n        new_test =  dataset.iloc[train.shape[0]:,:].reset_index(drop = True)\n    else:\n        new_train = dataset[:train.shape[0]]\n        new_test =  dataset[train.shape[0]:]\n    return new_train, new_test\''}, {'role': 'assistant', 'content': "['modelling']"}]