[{'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:from sklearn.metrics import euclidean_distances\nfrom sklearn.neighbors import KNeighborsClassifier\ndef plot_n_neighbors(X, y, n_neighbors=1):\nX_test = np.array([[0.5, 3], [4, 2], [-.5, .5]])\ndist = euclidean_distances(X, X_test)\nclosest = np.argsort(dist, axis=0)\nplt.figure()\nplt.gca().set_aspect("equal")\nfor x, neighbors in zip(X_test, closest.T):\nfor neighbor in neighbors[:n_neighbors]:\nplt.arrow(x[0], x[1], X[neighbor, 0] - x[0],\nX[neighbor, 1] - x[1], head_width=0, fc=\'k\', ec=\'k\')\nclf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)\ntest_points = plt.scatter(X_test[:, 0], X_test[:, 1], c=clf.predict(X_test), marker="*")\ntraining_points = plt.scatter(X[:, 0], X[:, 1], c=y)\nplot_n_neighbors(X, y)'}, {'role': 'assistant', 'content': 'modelling'}]