[{'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:# use a full grid over all parameters<br>\'\'\'<br>from sklearn.grid_search import GridSearchCV, RandomizedSearchCV<br>from time import time<br>param_grid = {"max_depth": [3, None],<br>              "max_features": [1, 3, 10],<br>              "min_samples_split": [1, 3, 10],<br>              "min_samples_leaf": [1, 3, 10],<br>              "bootstrap": [True, False]}<br><br>clf = RandomForestRegressor(n_estimators=20)<br># run grid search<br>grid_search = GridSearchCV(clf, param_grid=param_grid)<br>start = time()<br>grid_search.fit(X, y)<br><br>print("GridSearchCV took %.2f seconds for %d candidate parameter settings."<br>      % (time() - start, len(grid_search.grid_scores_)))<br>#report(grid_search.grid_scores_)<br>\'\'\''}, {'role': 'assistant', 'content': 'modelling'}]