[{'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:# set hyper parameters\nlgb_params = {"learning_rate": 0.01,\n              "num_leaves": 16,\n              "min_data_in_leaf": 20,\n              "boosting": "gbdt",\n              "num_iterations": 120,\n              "bagging_fraction": 0.6,\n              "feature_fraction": 1.0,\n              "seed": 42,\n              "num_threads": -1\n              }\n"""\nxgb_params = {"eta": 0.05,\n              "max_depth": 2,\n              "n_estimators": 120,\n              "objective": "binary:logistic",\n              "eval_metric": "logloss",\n              "booster": "gbtree",\n              "subsample": 0.6,\n              "colsample_bytree": 0.6,\n              "seed": 42,\n              "n_jobs": -1\n             }\n"""\n\n#model = multiclass.OneVsRestClassifier(ensemble.RandomForestClassifier(max_depth=7, n_estimators=1000, random_state=33))\n#model = multiclass.OneVsRestClassifier(xgb.XGBClassifier(**xgb_params))\nmodel = multiclass.OneVsRestClassifier(lgb.LGBMClassifier(**lgb_params))\n\n# 5 fold CV\nfolds = 5\nkf = KFold(n_splits=folds, shuffle=False, random_state=11)\ntrn = train[col].fillna(-1)\nval = train[["A", "B", "NEITHER"]]\nscores = []\ni = 0\n\nfor train_index, test_index in kf.split(train):\n    x1, x2 = trn.iloc[train_index], trn.iloc[test_index]\n    y1, y2 = val.iloc[train_index], val.iloc[test_index]\n\n    model.fit(x1, y1)\n    score = metrics.log_loss(y2, model.predict_proba(x2))\n    print(str(i+1), "log-loss:", score)\n    scores.append(score)\n    i += 1\n\nprint("CV Score(log-loss):", np.mean(scores))\n\n\nmodel.fit(train[col].fillna(-1), train[["A", "B", "NEITHER"]])\nresults = model.predict_proba(test[col])\ntest["A"] = results[:,0]\ntest["B"] = results[:,1]\ntest["NEITHER"] = results[:,2]\ntest[["ID", "A", "B", "NEITHER"]].to_csv("submission.csv", index=False)'}, {'role': 'assistant', 'content': 'modelling'}]