[{'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:from matplotlib.colors import ListedColormap\nfeature_names = [\n\'area\',\n\'perimeter\',\n\'compactness\',\n\'length of kernel\',\n\'width of kernel\',\n\'asymmetry coefficien\',\n\'length of kernel groove\',\n]\ndef train_plot(features, labels):\ny0,y1 = features[:,2].min()*.9, features[:,2].max()*1.1\nx0,x1 = features[:,0].min()*.9, features[:,0].max()*1.1\nX = np.linspace(x0,x1,100)\nY = np.linspace(y0,y1,100)\nX,Y = np.meshgrid(X,Y)\nmodel = learn_model(features[:,(0,2)], np.array(labels))\nC = apply_model(np.vstack([X.ravel(),Y.ravel()]).T, model).reshape(X.shape)\nif COLOUR_FIGURE:\ncmap = ListedColormap([(1.,.6,.6),(.6,1.,.6),(.6,.6,1.)])\nelse:\ncmap = ListedColormap([(1.,1.,1.),(.2,.2,.2),(.6,.6,.6)])\nplt.xlim(x0,x1)\nplt.ylim(y0,y1)\nplt.xlabel(feature_names[0])\nplt.ylabel(feature_names[2])\nplt.pcolormesh(X,Y,C, cmap=cmap)\nif COLOUR_FIGURE:\ncmap = ListedColormap([(1.,.0,.0),(.0,1.,.0),(.0,.0,1.)])\nplt.scatter(features[:,0], features[:,2], c=labels, cmap=cmap)\nelse:\nfor lab,ma in zip(range(3), "Do^"):\nplt.plot(features[labels == lab,0], features[labels == lab,2], ma, c=(1.,1.,1.))\nfeatures,labels = load_dataset(\'seeds\')\nnames = sorted(set(labels))\nlabels = np.array([names.index(ell) for ell in labels])\ntrain_plot(features, labels)\nplt.show()\nfeatures -= features.mean(0)\nfeatures /= features.std(0)\ntrain_plot(features, labels)\nplt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'modelling', 'result_visualization']"}]