[{'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:b"# Show info of model\\ndef show_info(model, X, y, log, weights = None):\\n    \'\'\'\\n    Show metrics about the evaluation model and plots about loss, rmse and rmspe\\n    \'\'\'\\n    if (log != None):\\n        # summarize history for loss\\n        plt.figure(figsize=(14,10))\\n        plt.plot(log.history[\'loss\'])\\n        plt.plot(log.history[\'val_loss\'])\\n        plt.title(\'Model Loss\')\\n        plt.ylabel(\'loss\')\\n        plt.xlabel(\'epoch\')\\n        plt.legend([\'train\', \'test\'], loc=\'upper left\')\\n        plt.show()\\n        print(\'\\\\n\')\\n        \\n        # summarize history for rmse\\n        plt.figure(figsize=(14,10))\\n        plt.plot(log.history[\'rmse\'])\\n        plt.plot(log.history[\'val_rmse\'])\\n        plt.title(\'Model RMSE\')\\n        plt.ylabel(\'rmse\')\\n        plt.xlabel(\'epoch\')\\n        plt.legend([\'train\', \'test\'], loc=\'upper left\')\\n        plt.show()\\n        print(\'\\\\n\')\\n        \\n        # summarize history for rmspe\\n        plt.figure(figsize=(14,10))\\n        plt.plot(log.history[\'rmspe\'])\\n        plt.plot(log.history[\'val_rmspe\'])\\n        plt.title(\'Model RMSPE\')\\n        plt.ylabel(\'rmspe\')\\n        plt.xlabel(\'epoch\')\\n        plt.legend([\'train\', \'test\'], loc=\'upper left\')\\n        plt.show()\\n\\n    if (weights != None):\\n        model.load_weights(weights)\\n\\n    predictions = model.predict(X, verbose=1)\\n\\n    mse = mean_squared_error(y, predictions)\\n    rmse = sqrt(mse)\\n    rmspe = rmspe_val(y, predictions)\\n\\n    print(\'MSE: %.3f\' % mse)\\n    print(\'RMSE: %.3f\' % rmse)\\n    print(\'RMSPE: %.3f\' % rmspe)"'}, {'role': 'assistant', 'content': 'modelling'}]