[{'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:class LGBWrapper(object):\n    """\n    A wrapper for lightgbm model so that we will have a single api for various models.\n    """\n\n    def __init__(self):\n        self.model = lgb.LGBMClassifier()\n\n    def fit(self, X_train, y_train, X_valid=None, y_valid=None, X_holdout=None, y_holdout=None, params=None):\n\n        eval_set = [(X_train, y_train)]\n        eval_names = [\'train\']\n        self.model = self.model.set_params(**params)\n\n        if X_valid is not None:\n            eval_set.append((X_valid, y_valid))\n            eval_names.append(\'valid\')\n\n        if X_holdout is not None:\n            eval_set.append((X_holdout, y_holdout))\n            eval_names.append(\'holdout\')\n\n        if \'cat_cols\' in params.keys():\n            cat_cols = [col for col in params[\'cat_cols\'] if col in X_train.columns]\n            if len(cat_cols) > 0:\n                categorical_columns = params[\'cat_cols\']\n            else:\n                categorical_columns = \'auto\'\n        else:\n            categorical_columns = \'auto\'\n\n        self.model.fit(X=X_train, y=y_train,\n                       eval_set=eval_set, eval_names=eval_names,\n                       verbose=params[\'verbose\'], early_stopping_rounds=params[\'early_stopping_rounds\'])\n\n        self.best_score_ = self.model.best_score_\n        self.feature_importances_ = self.model.feature_importances_\n\n    def predict_proba(self, X_test):\n        if self.model.objective == \'binary\':\n            return self.model.predict_proba(X_test, num_iteration=self.model.best_iteration_)[:, 1]\n        else:\n            return self.model.predict_proba(X_test, num_iteration=self.model.best_iteration_)\''}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]