[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:disease_names = ['psoriasis', 'seboreic dermatitis', 'lichen planus', 'pityriasis rosea',\n'cronic dermatitis', 'pityriasis rubra pilaris']\ncolors = ['red', 'turquoise', 'darkorange', 'purple', 'green', 'black']\nfig = plt.figure(figsize=(12,8))\nax3D = fig.add_subplot(111, projection='3d')\nfor color, disease_num, disease_name in zip(colors, list(range(1,7)), disease_names):\nax3D.scatter(X_r[y == disease_num, 0], X_r[y == disease_num, 1],\nX_r[y == disease_num, 2], c=color, s=50, alpha=1,\nedgecolor='k', label=disease_name, depthshade=False);\nax3D.view_init(elev=30,azim=60)\nax3D.set_xlabel('LDA Comp. 1')\nax3D.set_ylabel('LDA Comp. 2')\nax3D.set_zlabel('LDA Comp. 3')\nbox = ax3D.get_position()\nax3D.set_position([box.x0, box.y0, box.width * 0.8, box.height])\nplt.legend(loc='center left', bbox_to_anchor=(1, 0.5), scatterpoints=1)\nplt.title('LDA Projection of Dermatology Diseases');"}, {'role': 'assistant', 'content': "['result_visualization']"}]