[{'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:\n# show data on 2d plots\npositives = train_data_pca[train_labels[:]==1]\nnegatives = train_data_pca[train_labels[:]==0]\npositivest = test_data_pca[test_predicted[:]==1]\nnegativest = test_data_pca[test_predicted[:]==0]\n\nfor j in range(decompose_to):\n    plt.figure(figsize=(20,15))\n    for i in range(decompose_to):\n        plt.subplot(4,5,i+1)\n        axis = [j,i]\n        a=positives[:,axis]\n        plt.scatter(*zip(*a), color='r')\n        a=negatives[:,axis]\n        plt.scatter(*zip(*a), color='b')\n        plt.title(str(axis))\n    plt.show()\n    plt.figure(figsize=(20,15))\n    for i in range(decompose_to):\n        plt.subplot(4,5,i+1)\n        axis = [j,i]\n        a=positivest[:,axis]\n        plt.scatter(*zip(*a), color='g')\n        a=negativest[:,axis]\n        plt.scatter(*zip(*a), color='c')\n        plt.title(str(axis))\n    plt.show()"}, {'role': 'assistant', 'content': 'result_visualization'}]