[{'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:from sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(\nX, y, test_size=0.3, random_state=0)\nregr = linear_model.Ridge()\nX_train_no_intercept = X_train\nX_train = X_train.reshape(-1, X_train.shape[1])\nregr.fit(X_train, y_train)\nprint("Mean Squared Error, training data: %d"\n% np.mean((regr.predict(X_train) - y_train) ** 2))\nprint("Mean Squared Error, test data: %d"\n% np.mean((regr.predict(X_test) - y_test) ** 2))\nprint(30 * \'* \')\nprint(\'Intercept: \\n\', regr.intercept_)\nprint(\'Coefficients: \\n\', regr.coef_)\nprint("Residual sum of squares, training data: %.2f"\n% np.mean((regr.predict(X_train) - y_train) ** 2))\nprint("Residual sum of squares, test data: %.2f"\n% np.mean((regr.predict(X_test) - y_test) ** 2))\nvar_to_graph[\'multReg_ridge\'] = np.mean((regr.predict(X_test) - y_test) ** 2)\nprint(\'Variance score, training data: %.2f\' % regr.score(X_train, y_train))\nprint(\'Distribution of prediction error on training data:\')\npredError = regr.predict(X_train) - y_train\nplt.hist(predError)\nplt.show()\nprint(\'Distribution of prediction error on test data:\')\npredError = regr.predict(X_test) - y_test\nplt.hist(predError)\nplt.show()'}, {'role': 'assistant', 'content': 'modelling'}]