[{'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 sklearn import metrics\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.cluster import KMeans\nfrom sklearn.cluster import DBSCAN\nimport numpy as np\nn_clusters = 2\nmodel = KMeans(init=\'k-means++\', n_clusters=n_clusters, n_init=1).fit(data)\nmodel = DBSCAN(eps=0.15, min_samples=10).fit(data)\nlabels = model.labels_\nn_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\nprint(\'Estimated number of clusters: %d\' % n_clusters_)\nprint("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))\nprint("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))\nprint("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))\nprint("Adjusted Rand Index: %0.3f"\n% metrics.adjusted_rand_score(labels_true, labels))\nprint("Adjusted Mutual Information: %0.3f"\n% metrics.adjusted_mutual_info_score(labels_true, labels))\nprint("Silhouette Coefficient: %0.3f"\n% metrics.silhouette_score(data, labels))\nsilhouette_per_sample = metrics.silhouette_samples(data,labels)\ncentroids = []\nfor lab in range(0,n_clusters_):\ncentroids.append( [np.mean(data[np.where(labels==lab),0]), np.mean(data[np.where(labels==lab),1])] )\ncentroids = np.array(centroids)\nplt.figure()\nplt.scatter(data[:, 0], data[:, 1], c=labels,\ncmap=plt.cm.spectral, s=5, linewidths=0)\nif len(centroids)>0:\nplt.scatter(centroids[:, 0], centroids[:, 1],\nmarker=\'+\', s=200, linewidths=3, color=\'k\', zorder=10)  # plot the centroids\nplt.title(\'Estimated number of clusters: %d\' % n_clusters_)\nplt.figure()\nplt.scatter(data[:, 0], data[:, 1], c=silhouette_per_sample,\ncmap=plt.cm.gray, s=5, linewidths=0)\nplt.title(\'Clusters with silhouette coefficient coloring\')\nplt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'data_exploration', 'evaluation', 'modelling']"}]